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Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults
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Please cite this article as: Li X., Buxton O.M., Kim Y., Haneuse S. & Kawachi I., Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults, SSM - Population Health (2019), doi: https://doi.org/10.1016/j.ssmph.2019.100518. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults Xiaoyu Li, ScDa,b; Orfeu M. Buxton, PhDa,b,c,d; Yongjoo Kim, ScDa; Sebastien Haneuse, PhDe; Ichiro Kawachi, MD, PhDa a
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health,
Boston, MA; bDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; cDepartment of Biobehavioral Health, Pennsylvania State University, University Park, PA; dDivision of Sleep Medicine, Harvard Medical School, Boston, MA; eDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
Address correspondence to: Xiaoyu Li, ScD 401 Park Drive, Landmark Center 403-S, Boston, MA 02215 221 Longwood Avenue, BL252, Boston, MA 02115 Tel: (+1) 617-732-5987 Email: [email protected]
Declarations of interest: none.
1
1
Abstract
2
Procrastination is a widespread habit that has been understudied in the realm of health
3
behaviors, especially sleep. This study aimed to examine the cross-sectional relationships
4
between procrastination and multiple dimensions of sleep in a large national sample of US
5
adolescents and young adults. A random sample of 8,742 students from 11 US universities
6
provided self-reports of procrastination (measured by the General Procrastination Scale-Short
7
Form with scores ranging from 1 to 5) and sleep behaviors including social jetlag (the absolute
8
difference between mid-sleep times on weeknights and weekend nights), sleep duration (mean
9
weekly, weeknight, and weekend night), insomnia symptoms (trouble falling/staying asleep),
10
daytime sleepiness, and sleep medication use. Multiple linear regression and Poisson regression
11
models adjusted for socio-demographic and academic characteristics as well as response
12
propensity weights. Higher levels of procrastination were significantly associated with greater
13
social jetlag (β = 3.34 min per unit increase in the procrastination score; 95% CI [1.86, 4.81]),
14
shorter mean weekly sleep duration (β = -4.44 min; 95% CI [-6.36, -2.52]), and shorter
15
weeknight sleep duration (β = -6.10 min; 95% CI [-8.37, -3.84]), but not weekend night sleep
16
duration. Moreover, procrastination was associated with insomnia symptoms (Relative Risk (RR)
17
= 1.27; 95% CI [1.19, 1.37]) and daytime sleepiness (RR = 1.32; 95% CI [1.27, 1.38]), but not
18
sleep medication use. The results were robust to adjustment for anxiety and depressive
19
symptoms. Procrastination was associated with greater social jetlag, shorter sleep duration, and
20
worse sleep quality. If causal, the results suggest that interventions to prevent and manage
21
procrastination might help students to improve their sleep health.
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Keywords: procrastination, social jetlag, sleep duration, insomnia, adolescents, young adults
1
23
Introduction
24
Procrastination, or the tendency to voluntarily delay the beginning or completion of
25
intended tasks (despite expecting to be worse off for the delay), is a common form of self-
26
regulatory failure (Ferrari, 1993; Sirois & Pychyl, 2016). It is widely prevalent in modern
27
societies with potential deleterious consequences for many aspects of people’s lives (Ferrari,
28
O’Callaghan, & Newbegin, 2005; Steel & Ferrari, 2013). Although most individuals have
29
experienced putting off an action in order to avoid tasks that are associated with boredom,
30
anxiety, frustration, or resentment, certain segments of the population in technologically-
31
advanced societies (especially students) are known to suffer from chronic/habitual
32
procrastination. It is estimated that almost half of undergraduate and graduate students
33
habitually procrastinate on academic tasks such as writing a term paper, studying for an
34
examination, and keeping up with weekly reading assignments (Anthony J. Onwuegbuzie, 2004;
35
Hill, Hill, Chabot, & Barrall, 1978; Solomon & Rothblum, 1984). The procrastination prevalence
36
remains high despite the procrastinators’ stated intentions to reduce the behavior (O’Brien,
37
2000).
38
A substantial literature has documented the detrimental effects of procrastination in
39
different domains of life, such as completing work-related tasks, filing taxes on time, and saving
40
for retirement (Akerlof, 1991; Solomon & Rothblum, 1984; Steel, 2007; Steel, Brothen, &
41
Wambach, 2001; Wesley, 1994). By contrast, less attention has been paid to the effects of
42
procrastination in health behaviors and outcomes (Kroese, De Ridder, Evers, & Adriaanse, 2014;
43
Sirois, Melia-Gordon, & Pychyl, 2003). Procrastination is plausibly linked to behaviors that
44
exhibit the profile of “pain today – gain tomorrow”; for example, regular exercise, receiving
45
seasonal flu shots, regular dental checkups, or screening colonoscopy.(Sirois, 2007; Sirois et al., 2
46
2003) However, the relationship between procrastination and sleep has been minimally
47
explored. Initial investigations have indicated that procrastination increases vulnerability for
48
sleep problems (Hairston & Shpitalni, 2016; Kroese, De Ridder, et al., 2014; Kroese, Evers,
49
Adriaanse, & Ridder, 2014; Sirois, Eerde, & Argiropoulou, 2015). These findings, while
50
suggestive, are limited by the small samples involved and the few sleep measures used.
51
The Procrastination-Health Model proposed by Sirois and colleagues (Sirois et al., 2003)
52
could be used to explain the possible associations between procrastination and poor sleep. The
53
model posits that procrastination may affect health through both direct and indirect pathways.
54
The direct pathway relates to the excessive stress that habitual procrastinators frequently
55
experience from last-minute rushing to finish tasks or from missing deadlines (Sirois et al.,
56
2015). The indirect pathway concerns behavioral routes: procrastinators might engage more in
57
unhealthy behaviors that bring instant gratification and delay health-protective behaviors that
58
incur costs and efforts at the moment. Both stress and unhealthy behaviors are known
59
predictors of suboptimal sleep (Branstetter, Horton, Mercincavage, & Buxton, 2016; Lund,
60
Reider, Whiting, & Prichard, 2010; Phillips & Danner, 1995), and thus might explain the
61
relationships between procrastination and sleep.
62
Our goal is to assess the cross-sectional associations between chronic/habitual
63
procrastination and multiple dimensions of sleep (social jetlag, sleep duration, and sleep quality)
64
using data from a large national sample of US undergraduate and graduate students from the
65
Healthy Minds Study. To our knowledge, this study is the first to evaluate the relationship
66
between procrastination and social jetlag in a large national sample of adolescents and young
67
adults (Roenneberg, Allebrandt, Merrow, & Vetter, 2012; Wittmann, Dinich, Merrow, &
68
Roenneberg, 2006). Social jetlag reflects the discrepancy that arises between circadian and 3
69
social clocks (Roenneberg et al., 2012). The circadian clock regulates physiological processes
70
(e.g., sleep and energy homeostasis) to occur at distinct times over the course of a 24-hour day.
71
But people often use alarm clocks and/or medications to align their sleep and wake times with
72
social obligations (e.g., work and school schedules), fighting the circadian signals from the brain.
73
This results in differences in sleep timing between work/school and free days that lead to a
74
form of jetlag due to social factors. While the travel-induced jetlag is transient (symptoms
75
disappear when the clock re-entrains), social jetlag could be chronic throughout the years of
76
schooling and working (Roenneberg et al., 2012). Moreover, social jetlag has been linked to
77
obesity, psychological well-being, and consumption of stimulants (e.g., nicotine, alcohol, and
78
caffeine) (Wittmann et al., 2006). Given its high prevalence and negative health consequences,
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social jetlag is of high societal relevance. An understanding of how procrastination relates to
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social jetlag would shed light on whether procrastination behaviors also relate to circadian
81
misalignment, which might expose people to various health risk factors. This study is also
82
innovative in that it investigates the distinct associations between procrastination and mean
83
weekly sleep duration, weeknight sleep duration, and weekend night sleep duration, which
84
would shed light on a deeper understanding about how procrastination is related to quantity of
85
sleep on different types of days.
86
Materials and methods
87
Study participants and study design
88
Data came from the Healthy Minds Study (HMS) conducted by the Healthy Minds
89
Network for Research on Adolescent and Young Adult Mental Health (HMN). The HMS is an
90
ongoing nationwide annual web-based survey addressing mental health and related issues
91
among undergraduate and graduate students aged 18 and older. At each participating campus, 4
92
a random sample is selected from the full student population using the databases of school
93
registrars. Mostly this initial sample is 4,000 students. Since its establishment in 2007, HMS has
94
collected information on 200,000 survey respondents from over 180 colleges and universities.
95
The study was approved by the institutional review boards at all participating schools and all
96
participants gave informed consent. More information is available at
97
http://healthymindsnetwork.org/. The secondary data analyses were reviewed and approved
98
by the human subjects committee of the Harvard T.H. Chan School of Public Health.
99
This study used HMS data collected in 2011, the only year when questions about
100
procrastination were included. In 2011, 11 schools participated in the survey. Out of 33,257
101
students invited, 9,596 started the survey, among whom 92.4% completed it, yielding a sample
102
of 8,870 students (overall participation rate 26.7%).
103
We excluded participants who reported very short or very long sleep duration (< 3 hours
104
or > 13 hours) as they were likely to have serious sleep disorders. We also excluded participants
105
with extreme values of sleep timing (midpoint later than noon) and participants who were
106
potentially shift workers as they were likely to have irregular sleep patterns. Participants who
107
identified themselves as "transgender" were excluded as well since the group sample size (8)
108
was too small to make meaningful inferences. In total, 128 were excluded and the final sample
109
consisted of 8,742 participants (for data cleaning protocols, see Table A1).
110
Response propensity weights were constructed by the HMN to address the possibility
111
that students who completed the survey were different in important ways from those who did
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not. Administrative data on all students randomly selected for the study were provided by most
113
participating schools, including gender, race/ethnicity, academic level (undergraduate or
114
graduate), and GPA. These variables were used in a logistic regression to estimate the 5
115
probability of response, the inverse of which equaled response propensity weights. These
116
weights were used in all analyses.
117
Assessment of outcomes
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Social jetlag was quantified as the absolute difference between mid-sleep times on
119
weeknights and weekend nights. Respondents were asked at approximately what times they
120
had typically gone to sleep and woken up on weeknights and weekend nights during the school
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year (four separate questions). Apparent data entry errors in sleep timing reporting were
122
corrected (see details in supplementary materials). A person who went to sleep at midnight and
123
woke up at 7:00am during weeknights had a mid-point of sleep at 3:30am on weeknights. If this
124
person’s sleep onset and offset times shifted to 1:00am and 9:00am during weekend nights,
125
respectively, the mid-point of sleep on weekend nights would be 5:00am and he/she would
126
have a social jetlag of 1.5 hours.
127
Mean weekly sleep duration, weeknight sleep duration, and weekend night sleep
128
duration were assessed separately. Weeknight sleep duration and weekend night sleep duration
129
were evaluated by taking the differences between sleep offsets and onsets on weeknights and
130
weekend nights, respectively. Mean weekly sleep duration was calculated as the average
131
duration of five weeknights and two weekend nights.
132
Insomnia symptoms was measured by the question: “Over the last 4 weeks, how often
133
have you been bothered by trouble falling asleep or staying asleep?” Responses ranged from 1
134
(not at all) to 3 (more than half the days). Respondents who indicated that they had the
135
problems for more than half the days were regarded as suffering from insomnia symptoms. The
136
variable was dichotomized according to clinical relevance as experiencing insomnia most of the
137
time has been linked to increased risk of mortality (Li et al., 2014). 6
138
Daytime sleepiness was evaluated by the question: “Over the last 2 weeks, how often
139
have you been bothered by feeling tired or having little energy?” Responses were rated on a
140
scale ranging from 1 (not at all) to 4 (nearly every day). Those who were bothered by the
141
problem for more than half the days were defined as suffering from daytime sleepiness.
142
Sleep medication use was assessed by one yes-no item: “In the past month, have you
143
taken any prescribed sleep medications (e.g., zolpidem (Ambien), zaleplon (Sonata), etc.)?
144
Please count only those you took, or are taking, several times per week.”
145
Assessment of exposure
146
Procrastination was assessed by 3 items from a previously psychometrically-validated
147
scale – the General Procrastination Scale, which assesses the global tendencies towards
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chronic/habitual procrastination across a variety of daily tasks (Lay, 1986). In the 3-item
149
procrastination scale, respondents were asked how well the statements described themselves:
150
1) “I often find myself performing tasks that I had intended to do days before”, 2) “I generally
151
delay before starting on work I have to do”, and 3) “I am continually saying ‘I'll do it tomorrow’”.
152
Reponses to these items were given along a five-point Likert scale (extremely unlike me,
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moderately unlike me, neutral, moderately like me, and extremely like me). A continuous
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composite procrastination score was calculated by taking the mean of the items, with higher
155
values indicating higher levels of procrastination. The 3-item scale was estimated to have good
156
internal consistency reliability (Cronbach alpha = .86) in the 2011 HMS data. The variable was
157
centered at the sample mean in all regression analyses.
158
Covariates
159
Given that participants’ gender and age may determine both procrastination and sleep
160
behaviors (Steel, 2007; Wittmann et al., 2006), we controlled for these two variables to reduce 7
161
potential confounding bias. We also controlled for respondents’ sociodemographic and
162
academic characteristics to increase precision as they are known correlates of sleep (Ertel,
163
Berkman, & Buxton, 2011). Adjusted variables include race/ethnicity, relationship status, living
164
arrangement (with parents/guardians or not), past financial status, parental education (highest
165
level of education completed by mom or dad), academic level, and daily school work time.
166
Additionally, dummy variables of schools were included to account for systematic differences
167
that might affect students’ sleep such as school start times. In sensitivity analyses we also
168
adjusted for anxiety and depressive symptoms. Anxiety was assessed by the Patient Health
169
Questionnaire (PHQ) algorithm. Generalized anxiety was diagnosed if the subject had been
170
bothered by “feeling nervous, anxious, on edge, or worrying a lot about different things” for at
171
least more than half the days in the past 4 weeks and if 3 or more of the symptom criteria had
172
been present at least more than half the days (Spitzer, Kroenke, Williams, & Group, 1999).
173
Depressive symptoms were assessed by the PHQ-9, the depression module. Major depression
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was diagnosed if 5 or more of the symptom criteria had been present at least more than half
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the days in the past 2 weeks, and 1 of the symptoms was depressed mood or anhedonia
176
(Kroenke, Spitzer, & Williams, 2001).
177
Statistical analyses
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For the main analyses, we ran simple and multiple survey linear regression models to
179
examine the associations of procrastination with social jetlag and sleep duration. Survey
180
Poisson regression models were used to evaluate the relationships between procrastination
181
with binary sleep quality indicators (insomnia symptoms, daytime sleepiness, and sleep
182
medication use) (Zou, 2004). To estimate 95% confidence intervals for the relative risks and
183
Wald-based p-values, we used the robust sandwich estimator of the variance. Sensitivity 8
184
analyses were conducted to assess if the results were influenced by data cleaning by excluding
185
observations with identified sleep timing data entry errors. We also conducted sensitivity
186
analyses by adjusting anxiety and depressive symptoms. Additionally, we assessed the
187
relationships between the sleep outcomes and procrastination based on quartiles of the
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procrastination score. Inverse probability weighting were applied to all analyses using weights
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provided by HMN to improve representativeness of the full student population in terms of the
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available administrative variables (Seaman & White, 2013). All analyses were performed using STATA version 15.1 (StataCorp LP, College Station,
191 192
Texas). All tests were two-sided, with a significance level of 5%.
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Results
194
Descriptive traits of the analytic sample are presented in Table 1. Slightly more than half
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(58.0%) of the weighted sample were female. About three-quarters (78.6%) of the total were
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between the ages 18 and 22. The racial/ethnic composition was 77.2% white, 7.0% black, 5.3%
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Hispanic/Latino, 4.5% Asian, and 5.9% other. Approximately 1 in two (52.9%) were single; 9.9%
198
were living with parents or guardians. Few participants (2.7%) grew up in a very poor financial
199
situation without enough to get by, 29.7% enough to get by, about half (56.3%) comfortable,
200
and 11.3% well to do; 12.0% had a parent with high school education or less, 22.5% with an
201
associate’s degree or some college, 29.5% with a college degree, 36.1% with a graduate degree.
202
The majority (81.1%) were undergraduate students. Slightly more than one-third (39.8%) spent
203
2 hours or less per day on school work, 36.2% spent 3-4 hours, and 24.0% spent 5 hours or
204
more.
205 206
The mean of procrastination score was 3.3 (SD: 1.1) on a 1-5 scale (see Figure 1 for its distribution). The mean of social jetlag was 1.8 hours (SD: 1.0). Figure 2A presents its 9
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distribution. About four-fifths (81.3%) of the sample suffered from at least one hour of social
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jetlag. Figure 2B presents the relationship between social jetlag and age, highlighting that social
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jetlag peaked at the end of adolescence and decreased afterwards. Respondents slept longer
210
on weekend nights (mean: 8.6 hours; SD: 1.4) than on weeknights (mean: 7.8 hours; SD: 1.5);
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the mean of weekly average sleep duration was 8.1 hours (SD: 1.3) (see Figure 3 for
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distributions of sleep duration). About one-fifth were bothered by insomnia symptoms, 31.6 %
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daytime sleepiness, and 3.9% sleep medication use.
214
Table 2 reports results from multiple linear regression models separately estimating social
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jetlag, mean weekly sleep duration, weeknight sleep duration, and weekend night sleep
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duration (all assessed in minutes). Procrastination was positively linked to social jetlag (β = 3.34
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min; 95% CI [1.86, 4.81]), indicating that procrastinators suffer more circadian misalignment
218
than non-procrastinators.
219
For sleep duration, higher procrastination scores were significantly associated with
220
shorter mean weekly sleep duration (β = -4.44 min; 95% CI [-6.36, -2.52]) and shorter weeknight
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sleep duration (β = -6.10 min; 95% CI [-8.37, -3.84]), after controlling for sociodemographic and
222
academic factors. On the other hand, procrastination was not significantly associated with
223
weekend night sleep duration. Thus, the shorter mean weekly sleep duration experienced by
224
procrastinators were driven by their shorter weeknight sleep duration. Specifically, with a one-
225
unit increase in procrastination score, weeknight sleep duration decreased by 6 minutes,
226
resulting in a total 24 minutes sleep/night difference between the lowest and the highest levels
227
of procrastination per night. For mean weekly sleep duration, an average of 4 minutes less
228
sleep per night was associated with a one-unit increase in procrastination score, which was
229
approximately half an hour per week decrease in sleep. 10
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Table 3 reports results from multiple Poisson regression models separately estimating
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indices of sleep quality, including insomnia symptoms, daytime sleepiness, and sleep
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medication use. Higher levels of procrastination were associated with elevated risks of
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insomnia symptoms (Relative Risk (RR) = 1.27; 95% CI [1.19, 1.37]) and daytime sleepiness (RR =
234
1.32; 95% CI [1.27, 1.38]). However, there was no evidence that procrastination was associated
235
with sleep medication use.
236
In sensitivity analyses, we re-estimated all models restricting the sample to respondents
237
with no data entry errors to evaluate if data cleaning had an impact on the inferences, as well
238
as adjusting for anxiety and depressive symptoms. Results were similar to those from the main
239
analyses, which spoke to the robustness of our findings. Results based on quartiles of the
240
procrastination score were similar to those from the main analyses as well. Specifically,
241
compared to individuals in the lowest quartile of the procrastination score, those in the highest
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quartile of the procrastination score had greater social jetlag (β = 10.98 min; 95% CI [6.30,
243
15.65]), shorter mean weekly sleep duration (β = -15.34 min; 95% CI [-21.45, -9.24]), and
244
shorter weeknight sleep duration (β = -20.52 min; 95% CI [-27.73, -13.31]) (Table A2). Those
245
who procrastinated the most also had greater odds of insomnia symptoms (RR = 2.06; 95% CI
246
[1.70, 2.51]) and daytime sleepiness (RR = 2.21; 95% CI [1.96, 2.49]) compared to those who
247
procrastinated the least (Table A3).
248
Discussion
249
This study investigated the relationships between self-reported procrastination and a
250
variety of sleep behaviors (social jetlag, sleep duration, insomnia symptoms, daytime sleepiness,
251
and sleep mediation use) among US undergraduate and graduate students. Procrastination was
252
positively associated with social jetlag, a delay in sleep timing from weeknights to weekend 11
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nights, indicating that procrastinators experienced more circadian misalignment than non-
254
procrastinators. Second, higher levels of procrastination were associated with shorter mean
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weekly sleep duration driven by shorter weeknight sleep duration experienced by
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procrastinators. In contrast, weekend night sleep duration was not linked to procrastination.
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Lastly, greater procrastination was also associated with increased risks of insomnia symptoms
258
and daytime sleepiness, but not sleep medication use.
259
While this study, guided by the Procrastination-Health Model, postulated that
260
procrastination might be a determinant of poor sleep. Some prior studies have, on the other
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hand, argued that the causal direction could be the opposite (van Eerde & Venus, 2018).
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Namely, sleep deprivation results in depleted self-control resources, which make the affected
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individuals more likely to procrastinate (Kühnel, Bledow, & Feuerhahn, 2016). There might
264
indeed exist feedback loops between procrastination and sleep such that procrastination
265
compromises sleep, which in turn results in a reduction in self-regulation resources, which
266
further exacerbates the tendency to procrastinate. Yet, research has shown that chronic
267
procrastination is trait like in that there might be genetic underpinnings to chronic
268
procrastination measured by the General Procrastination Scale (Gustavson, Miyake, Hewitt, &
269
Friedman, 2014, 2015), making it conceptually difficult to argue that over a short period of time
270
that lack of sleep would turn into chronic/habitual procrastination.
271
Kroese and colleagues introduced the concept of bedtime procrastination, “failing to go
272
to bed at the intended time, while no external circumstances prevent a person from doing so”
273
(Kroese, De Ridder, et al., 2014; Kroese, Evers, et al., 2014). They found that bedtime
274
procrastination was linked to reports of insufficient sleep in an Amazon Mechanical Turk
275
sample (N = 177) and a Dutch adult sample (N = 2,431) (Kroese, De Ridder, et al., 2014; Kroese, 12
276
Evers, et al., 2014). However, these two studies addressed only one specific domain of
277
procrastination – bedtime procrastination, but not general procrastination. Two other studies
278
examined the link between general procrastination and sleep. Hairston surveyed 598
279
individuals and found that procrastination was related to insomnia symptoms (Hairston &
280
Shpitalni, 2016). Sirois and colleagues evaluated the problem in two samples, one of Greek
281
undergraduate students (N = 141) and one of Canadian students (N = 339). Results revealed
282
that procrastination was associated with indices of poor sleep quality and that higher stress
283
seemed to play a role in explaining the association (Sirois et al., 2015). Our finding that
284
procrastination was linked to insomnia symptoms and daytime sleepiness is consistent with
285
previous findings (Hairston & Shpitalni, 2016; Sirois et al., 2015). A prior study found an
286
association between procrastination and sleep medication use (Sirois et al., 2015) whereas we
287
did not. A reason for the difference could be that the question in the HMS survey was very strict
288
in asking respondents to only count “prescribed” sleep medications they took or were taking
289
“several times per week”, which could signal serious sleep disorders. In contrast, other surveys
290
usually ask respondents how often they took prescribed or over-the-counter sleep medications.
291
The fact that procrastination was only associated with weeknight but not weekend night
292
sleep duration could arise from different sleep patterns (times) on weeknights and weekend
293
nights based on levels of procrastination. Specifically, procrastinators had later sleep onsets
294
compared to non-procrastinators on both weeknights and weekend nights (observation from
295
the HMS data, summary available upon request). This is consistent with prior research showing
296
that procrastinators tend to be evening owls (Díaz-Morales, Ferrari, & Cohen, 2008; Digdon &
297
Howell, 2008). This resulted in shorter sleep duration on weeknights (because of early sleep
298
offsets – schools start at the same time regardless of students’ bedtime) and slightly longer 13
299
sleep duration on weekend nights (because of late sleep offsets thanks to fewer external
300
constraints on free days). Procrastinators’ shifted sleep times between weeknights and
301
weekend nights also explained the greater social jetlag they experienced: the difference
302
between their mid-sleep times on weekdays and weekends were bigger than that of non-
303
procrastinators’.
304
There are a few possible explanations for procrastinators’ late sleep onsets. First,
305
procrastinators might have to stay up late to finish the tasks they had postponed doing (thereby
306
displacing the hours available for sleep). Second, they might simply delay the action of going to
307
bed (bedtime procrastination (Kroese, De Ridder, et al., 2014)) by not quitting other activities
308
such as checking social media or devices, or other screen-based activities. The extra (primarily
309
blue) light exposure they receive (e.g., from using electronic devices) before bedtime may
310
additionally suppress melatonin levels (Chang, Aeschbach, Duffy, & Czeisler, 2015), which in
311
turn would delay sleep onsets.
312
One emerging sleep health component related to social jetlag is sleep regularity, which is
313
important for understanding sleep disorders such as insomnia and circadian rhythm disorders
314
(Buysse, 2014). Studies have linked sleep regularity to cardiometabolic outcomes – increased
315
variability in sleep duration and timing was associated with metabolic abnormalities (Huang &
316
Redline, 2019; Taylor et al., 2016). While social jetlag describes sleep timing differences
317
between weeknights and weekend nights, sleep regularity captures more general, night-to-
318
night variability in sleep patterns. Procrastination might be more strongly associated with night-
319
to-night sleep regularity than social jetlag, since sleep schedules are more likely to be
320
influenced by procrastination behaviors on a daily basis depending on whether there is an
14
321
urgent task. Although our data did not allow us to assess sleep regularity, future research might
322
consider investigating the relationship between procrastination and sleep regularity.
323
This study contributes to the literature in four ways. First, our study is the first that has
324
evaluated the relationship between procrastination and social jetlag in a large national student
325
sample. Second, this study is innovative in that it investigates the distinct associations between
326
procrastination and mean weekly sleep duration, weeknight sleep duration, and weekend night
327
sleep duration. Prior research has only investigated whether procrastination is linked to
328
average/typical sleep duration during the past month (Sirois et al., 2015) or just weekday sleep
329
duration (Kroese, De Ridder, et al., 2014). A differentiation between mean weekly, weeknight,
330
and weekend night sleep duration would elucidate the how procrastination is tied to sleep
331
duration on different types of days. Third, we investigate the relationships between
332
procrastination with indices of sleep quality, including insomnia symptoms, daytime sleepiness,
333
and sleep medication use. Combined with social jetlag and sleep duration, these different
334
aspects of sleep paint a comprehensive picture of sleep health. Fourth, this study focuses on US
335
adolescents and young adults, populations that suffer from serious problems of both
336
procrastination and sleep deprivation (Edens, 2006; Hill et al., 1978). The large national sample
337
used would afford generalizable insights on procrastination and sleep among US undergraduate
338
and graduate students.
339
This study has limitations. First, the cross-sectional nature of the data constrained our
340
ability to make causal inferences. Given the potential cyclic relationship between
341
procrastination and sleep, more studies with longitudinal or experimental designs are
342
warranted. Efforts could also be devoted to investigating the potential mediating roles of health
343
behaviors. Second, while applying the response propensity weights constructed by the HMN to 15
344
analyses increased our confidence that the weighted estimates were representative of the full
345
student population in terms of administrative variables, our results were not immune from
346
selection bias as it was possible that students who were procrastinators and whose sleep was
347
suboptimal were less likely to respond to the survey. Lastly, both procrastination and sleep
348
measures were self-reported by respondents in the HMS data, which might induce common-
349
method bias. Prior research has shown that self-reported sleep duration overestimated sleep
350
duration by about 1 hour when compared with actigraphy-measured total sleep time or
351
polysomnography-defined sleep time. The self-reported sleep only moderately correlated with
352
objectively measured sleep (Jackson, Patel, Jackson, Lutsey, & Redline, 2018). Furthermore,
353
procrastination might affect the accuracy of individuals’ self-reported sleep times and the
354
extent of over-reporting of sleep duration, or affect how individuals perceive their sleep quality,
355
which might result in differential classification of the sleep outcomes. If the inaccurate
356
reporting is non-differential with respect to procrastination, it would drive the associations
357
towards the null. However, if there is differential misclassification – e.g., procrastinators over-
358
estimate how much sleep they are getting compared to non-procrastinators, this would drive
359
associations away from the null (the actual associations might be stronger than what we
360
observed). It would be of interest for future research to use objective measures of sleep (e.g.,
361
longitudinal actigraphy data) to elucidate the relationships between procrastination and
362
multiple dimensions of sleep.
363
Although the magnitudes of the observed associations are modest, the effect sizes for the
364
sleep outcomes, when accumulated over an extended period of time for habitual
365
procrastinators, might have substantial implications on their health and cognitive performance.
366
For instance, the Sleep Continuity Theory posits that a period of 10 minutes of uninterrupted 16
367
sleep is required for restoration to take place (Downey & Bonnet, 1987). Experimental research
368
has shown that sleeping as brief as 10 minutes could improve subjective and objective alertness
369
and increase feelings of vigor and decrease fatigue (Lovato & Lack, 2010).
370
Conclusions
371
Our study showed that procrastination was associated with more social jetlag, shorter
372
sleep duration, and worse sleep quality among US undergraduate and graduate students. The
373
findings highlight that procrastination might be a vulnerability factor for poor health, but not
374
just something prevalent and relatively harmless. With the increasing prevalence and
375
availability of temptations (e.g., ubiquity of electronic devices and 24/7 entertainment industry),
376
people are facing more distractions than before and the problem of procrastination might
377
continue to increase (Kroese, Evers, et al., 2014; Steel, 2007). Findings from this study inform
378
potential targeted interventions to help adolescents and young adults prevent and manage
379
procrastination and to improve their sleep health, which in turn, could promote their overall
380
well-being (mental and physical health) and performance. Procrastination treatment programs
381
that improve people’s time management skills would be of value, as would recommendations
382
tailored to reduce procrastination on going to bed. For instance, by simply adjusting proximity
383
to temptations (e.g., keep the cell-phone out of the bedroom), procrastination could be
384
reduced. In addition, training programs targeting emotion regulation skills might also be of
385
value, as research has shown that training of emotion-focused strategies decreases
386
procrastination (Marcus Eckert, David D Ebert, Dirk Lehr, Bernhard Sieland, & Matthias Berking,
387
2016).
17
388
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Table 1. Descriptive traits of HMS cohort, N=8,742 % / Mean(SD) Socio-demographics Gender Female 58.0% Male 42.0% Age (y) 18-22 78.6% 23-25 8.6% 26-30 5.5% 31+ 7.3% Race/ethnicity White 77.2% Black 7.0% Hispanic/Latino 5.3% Asian 4.5% Other 5.9% Relationship status Single 52.9% In a relationship 37.0% Married 9.3% Divorced/widowed 0.7% Living with parents or guardians Yes 9.9% No 90.1% Past financial situation Very poor, not enough to get by 2.7% Enough to get by 29.7% Comfortable 56.3% Well to do 11.3% Parental education High school or less 12.0% Associate's degree or some college 22.5% College degree 29.5% Graduate degree 36.1% Response propensity weights applied.
% / Mean(SD) Academic factors Degree level Undergraduate Graduate Other School work time (h/day) <= 2 3-4 >= 5 Mental health factors Major depression Yes No
81.1% 12.1% 6.9% 39.8% 36.2% 24.0%
9.8% 90.2%
Generalized anxiety Yes No Procrastination Procrastination (1-5 scale) Sleep Social jetlag (h/week) Mean weekly sleep duration (h/day) Weeknight sleep duration (h/day) Weekend night sleep duration (h/day) Insomnia symptoms Yes No Daytime sleepiness Yes No Sleep medication use Yes No
5.9% 94.1% 3.32 (1.10) 1.76 (.99) 8.06 (1.26) 7.84 (1.46) 8.60 (1.40) 20.8% 79.2% 31.6% 68.4% 3.9% 96.1%
Table 2. Linear regression models estimating continuous sleep outcomes Social jetlag
Mean weekly duration
Weeknight duration
Weekend night duration
Beta [95% CI]
Beta [95% CI]
Beta [95% CI]
Beta [95% CI]
5.26 [3.75,6.78]***
-2.11 [-4.07,-0.15]*
-3.41 [-5.70,-1.12]**
1.13 [-0.98,3.25]
3.34 [1.86,4.81]***
-4.44 [-6.36,-2.52]***
-6.10 [-8.37,-3.84]***
-0.29 [-2.39,1.82]
Procrastination (centered) Unadjusted Adjusted 95% confidence intervals in brackets. +
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Adjusted analyses controlled for gender, age, race/ethnicity, relationship status, living arrangement, past financial status, parental education, academic level, daily school work time, and school dummies. Response propensity weights applied.
Table 3. Poisson regression models estimating binary sleep outcomes Insomnia symptoms
Daytime sleepiness
RR [95% CI]
RR [95% CI]
Sleep medication use RR [95% CI]
Procrastination (centered) Unadjusted
1.27 [1.18,1.36]***
1.27 [1.19,1.37]*** Adjusted Exponentiated coefficients; 95% confidence intervals in brackets. +
1.31 [1.26,1.37]***
1.09 [0.93,1.29]
1.32 [1.27,1.38]***
1.11 [0.95,1.29]
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Adjusted analyses controlled for gender, age, race/ethnicity, relationship status, living arrangement, past financial status, parental education, academic level, daily school work time, and school dummies. Response propensity weights applied.
Figure 1. Distribution of procrastination score
Figure 2. Distribution of social jetlag (A) and average social jetlag as a function of age (B)
Figure 3. Distributions of sleep durations
Research highlights -
Procrastination is associated with multiple dimensions of sleep in adolescents and young adults.
-
Procrastination is associated with greater social jetlag.
-
Procrastination is associated with shorter sleep duration.
-
Procrastination is associated with increased risk of insomnia symptoms and daytime sleepiness.
Title Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults Authors Xiaoyu Li, ScDa,b; Orfeu M. Buxton, PhDa,b,c,d; Yongjoo Kim, ScDa; Sebastien Haneuse, PhDe; Ichiro Kawachi, MD, PhDa a
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public
Health, Boston, MA; bDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; cDepartment of Biobehavioral Health, Pennsylvania State University, University Park, PA; dDivision of Sleep Medicine, Harvard Medical School, Boston, MA; eDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA Ethics approval The study was approved by the institutional review boards at all participating schools and all participants gave informed consent. More information is available at http://healthymindsnetwork.org/. The secondary data analyses were reviewed and approved by the human subjects committee of the Harvard T.H. Chan School of Public Health. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study.
S2352-8273(19)30336-2
DOI:
https://doi.org/10.1016/j.ssmph.2019.100518
Reference:
SSMPH 100518
To appear in:
SSM - Population Health
Received Date: 4 October 2019 Revised Date:
7 November 2019
Accepted Date: 15 November 2019
Please cite this article as: Li X., Buxton O.M., Kim Y., Haneuse S. & Kawachi I., Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults, SSM - Population Health (2019), doi: https://doi.org/10.1016/j.ssmph.2019.100518. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults Xiaoyu Li, ScDa,b; Orfeu M. Buxton, PhDa,b,c,d; Yongjoo Kim, ScDa; Sebastien Haneuse, PhDe; Ichiro Kawachi, MD, PhDa a
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health,
Boston, MA; bDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; cDepartment of Biobehavioral Health, Pennsylvania State University, University Park, PA; dDivision of Sleep Medicine, Harvard Medical School, Boston, MA; eDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
Address correspondence to: Xiaoyu Li, ScD 401 Park Drive, Landmark Center 403-S, Boston, MA 02215 221 Longwood Avenue, BL252, Boston, MA 02115 Tel: (+1) 617-732-5987 Email: [email protected]
Declarations of interest: none.
1
1
Abstract
2
Procrastination is a widespread habit that has been understudied in the realm of health
3
behaviors, especially sleep. This study aimed to examine the cross-sectional relationships
4
between procrastination and multiple dimensions of sleep in a large national sample of US
5
adolescents and young adults. A random sample of 8,742 students from 11 US universities
6
provided self-reports of procrastination (measured by the General Procrastination Scale-Short
7
Form with scores ranging from 1 to 5) and sleep behaviors including social jetlag (the absolute
8
difference between mid-sleep times on weeknights and weekend nights), sleep duration (mean
9
weekly, weeknight, and weekend night), insomnia symptoms (trouble falling/staying asleep),
10
daytime sleepiness, and sleep medication use. Multiple linear regression and Poisson regression
11
models adjusted for socio-demographic and academic characteristics as well as response
12
propensity weights. Higher levels of procrastination were significantly associated with greater
13
social jetlag (β = 3.34 min per unit increase in the procrastination score; 95% CI [1.86, 4.81]),
14
shorter mean weekly sleep duration (β = -4.44 min; 95% CI [-6.36, -2.52]), and shorter
15
weeknight sleep duration (β = -6.10 min; 95% CI [-8.37, -3.84]), but not weekend night sleep
16
duration. Moreover, procrastination was associated with insomnia symptoms (Relative Risk (RR)
17
= 1.27; 95% CI [1.19, 1.37]) and daytime sleepiness (RR = 1.32; 95% CI [1.27, 1.38]), but not
18
sleep medication use. The results were robust to adjustment for anxiety and depressive
19
symptoms. Procrastination was associated with greater social jetlag, shorter sleep duration, and
20
worse sleep quality. If causal, the results suggest that interventions to prevent and manage
21
procrastination might help students to improve their sleep health.
22
Keywords: procrastination, social jetlag, sleep duration, insomnia, adolescents, young adults
1
23
Introduction
24
Procrastination, or the tendency to voluntarily delay the beginning or completion of
25
intended tasks (despite expecting to be worse off for the delay), is a common form of self-
26
regulatory failure (Ferrari, 1993; Sirois & Pychyl, 2016). It is widely prevalent in modern
27
societies with potential deleterious consequences for many aspects of people’s lives (Ferrari,
28
O’Callaghan, & Newbegin, 2005; Steel & Ferrari, 2013). Although most individuals have
29
experienced putting off an action in order to avoid tasks that are associated with boredom,
30
anxiety, frustration, or resentment, certain segments of the population in technologically-
31
advanced societies (especially students) are known to suffer from chronic/habitual
32
procrastination. It is estimated that almost half of undergraduate and graduate students
33
habitually procrastinate on academic tasks such as writing a term paper, studying for an
34
examination, and keeping up with weekly reading assignments (Anthony J. Onwuegbuzie, 2004;
35
Hill, Hill, Chabot, & Barrall, 1978; Solomon & Rothblum, 1984). The procrastination prevalence
36
remains high despite the procrastinators’ stated intentions to reduce the behavior (O’Brien,
37
2000).
38
A substantial literature has documented the detrimental effects of procrastination in
39
different domains of life, such as completing work-related tasks, filing taxes on time, and saving
40
for retirement (Akerlof, 1991; Solomon & Rothblum, 1984; Steel, 2007; Steel, Brothen, &
41
Wambach, 2001; Wesley, 1994). By contrast, less attention has been paid to the effects of
42
procrastination in health behaviors and outcomes (Kroese, De Ridder, Evers, & Adriaanse, 2014;
43
Sirois, Melia-Gordon, & Pychyl, 2003). Procrastination is plausibly linked to behaviors that
44
exhibit the profile of “pain today – gain tomorrow”; for example, regular exercise, receiving
45
seasonal flu shots, regular dental checkups, or screening colonoscopy.(Sirois, 2007; Sirois et al., 2
46
2003) However, the relationship between procrastination and sleep has been minimally
47
explored. Initial investigations have indicated that procrastination increases vulnerability for
48
sleep problems (Hairston & Shpitalni, 2016; Kroese, De Ridder, et al., 2014; Kroese, Evers,
49
Adriaanse, & Ridder, 2014; Sirois, Eerde, & Argiropoulou, 2015). These findings, while
50
suggestive, are limited by the small samples involved and the few sleep measures used.
51
The Procrastination-Health Model proposed by Sirois and colleagues (Sirois et al., 2003)
52
could be used to explain the possible associations between procrastination and poor sleep. The
53
model posits that procrastination may affect health through both direct and indirect pathways.
54
The direct pathway relates to the excessive stress that habitual procrastinators frequently
55
experience from last-minute rushing to finish tasks or from missing deadlines (Sirois et al.,
56
2015). The indirect pathway concerns behavioral routes: procrastinators might engage more in
57
unhealthy behaviors that bring instant gratification and delay health-protective behaviors that
58
incur costs and efforts at the moment. Both stress and unhealthy behaviors are known
59
predictors of suboptimal sleep (Branstetter, Horton, Mercincavage, & Buxton, 2016; Lund,
60
Reider, Whiting, & Prichard, 2010; Phillips & Danner, 1995), and thus might explain the
61
relationships between procrastination and sleep.
62
Our goal is to assess the cross-sectional associations between chronic/habitual
63
procrastination and multiple dimensions of sleep (social jetlag, sleep duration, and sleep quality)
64
using data from a large national sample of US undergraduate and graduate students from the
65
Healthy Minds Study. To our knowledge, this study is the first to evaluate the relationship
66
between procrastination and social jetlag in a large national sample of adolescents and young
67
adults (Roenneberg, Allebrandt, Merrow, & Vetter, 2012; Wittmann, Dinich, Merrow, &
68
Roenneberg, 2006). Social jetlag reflects the discrepancy that arises between circadian and 3
69
social clocks (Roenneberg et al., 2012). The circadian clock regulates physiological processes
70
(e.g., sleep and energy homeostasis) to occur at distinct times over the course of a 24-hour day.
71
But people often use alarm clocks and/or medications to align their sleep and wake times with
72
social obligations (e.g., work and school schedules), fighting the circadian signals from the brain.
73
This results in differences in sleep timing between work/school and free days that lead to a
74
form of jetlag due to social factors. While the travel-induced jetlag is transient (symptoms
75
disappear when the clock re-entrains), social jetlag could be chronic throughout the years of
76
schooling and working (Roenneberg et al., 2012). Moreover, social jetlag has been linked to
77
obesity, psychological well-being, and consumption of stimulants (e.g., nicotine, alcohol, and
78
caffeine) (Wittmann et al., 2006). Given its high prevalence and negative health consequences,
79
social jetlag is of high societal relevance. An understanding of how procrastination relates to
80
social jetlag would shed light on whether procrastination behaviors also relate to circadian
81
misalignment, which might expose people to various health risk factors. This study is also
82
innovative in that it investigates the distinct associations between procrastination and mean
83
weekly sleep duration, weeknight sleep duration, and weekend night sleep duration, which
84
would shed light on a deeper understanding about how procrastination is related to quantity of
85
sleep on different types of days.
86
Materials and methods
87
Study participants and study design
88
Data came from the Healthy Minds Study (HMS) conducted by the Healthy Minds
89
Network for Research on Adolescent and Young Adult Mental Health (HMN). The HMS is an
90
ongoing nationwide annual web-based survey addressing mental health and related issues
91
among undergraduate and graduate students aged 18 and older. At each participating campus, 4
92
a random sample is selected from the full student population using the databases of school
93
registrars. Mostly this initial sample is 4,000 students. Since its establishment in 2007, HMS has
94
collected information on 200,000 survey respondents from over 180 colleges and universities.
95
The study was approved by the institutional review boards at all participating schools and all
96
participants gave informed consent. More information is available at
97
http://healthymindsnetwork.org/. The secondary data analyses were reviewed and approved
98
by the human subjects committee of the Harvard T.H. Chan School of Public Health.
99
This study used HMS data collected in 2011, the only year when questions about
100
procrastination were included. In 2011, 11 schools participated in the survey. Out of 33,257
101
students invited, 9,596 started the survey, among whom 92.4% completed it, yielding a sample
102
of 8,870 students (overall participation rate 26.7%).
103
We excluded participants who reported very short or very long sleep duration (< 3 hours
104
or > 13 hours) as they were likely to have serious sleep disorders. We also excluded participants
105
with extreme values of sleep timing (midpoint later than noon) and participants who were
106
potentially shift workers as they were likely to have irregular sleep patterns. Participants who
107
identified themselves as "transgender" were excluded as well since the group sample size (8)
108
was too small to make meaningful inferences. In total, 128 were excluded and the final sample
109
consisted of 8,742 participants (for data cleaning protocols, see Table A1).
110
Response propensity weights were constructed by the HMN to address the possibility
111
that students who completed the survey were different in important ways from those who did
112
not. Administrative data on all students randomly selected for the study were provided by most
113
participating schools, including gender, race/ethnicity, academic level (undergraduate or
114
graduate), and GPA. These variables were used in a logistic regression to estimate the 5
115
probability of response, the inverse of which equaled response propensity weights. These
116
weights were used in all analyses.
117
Assessment of outcomes
118
Social jetlag was quantified as the absolute difference between mid-sleep times on
119
weeknights and weekend nights. Respondents were asked at approximately what times they
120
had typically gone to sleep and woken up on weeknights and weekend nights during the school
121
year (four separate questions). Apparent data entry errors in sleep timing reporting were
122
corrected (see details in supplementary materials). A person who went to sleep at midnight and
123
woke up at 7:00am during weeknights had a mid-point of sleep at 3:30am on weeknights. If this
124
person’s sleep onset and offset times shifted to 1:00am and 9:00am during weekend nights,
125
respectively, the mid-point of sleep on weekend nights would be 5:00am and he/she would
126
have a social jetlag of 1.5 hours.
127
Mean weekly sleep duration, weeknight sleep duration, and weekend night sleep
128
duration were assessed separately. Weeknight sleep duration and weekend night sleep duration
129
were evaluated by taking the differences between sleep offsets and onsets on weeknights and
130
weekend nights, respectively. Mean weekly sleep duration was calculated as the average
131
duration of five weeknights and two weekend nights.
132
Insomnia symptoms was measured by the question: “Over the last 4 weeks, how often
133
have you been bothered by trouble falling asleep or staying asleep?” Responses ranged from 1
134
(not at all) to 3 (more than half the days). Respondents who indicated that they had the
135
problems for more than half the days were regarded as suffering from insomnia symptoms. The
136
variable was dichotomized according to clinical relevance as experiencing insomnia most of the
137
time has been linked to increased risk of mortality (Li et al., 2014). 6
138
Daytime sleepiness was evaluated by the question: “Over the last 2 weeks, how often
139
have you been bothered by feeling tired or having little energy?” Responses were rated on a
140
scale ranging from 1 (not at all) to 4 (nearly every day). Those who were bothered by the
141
problem for more than half the days were defined as suffering from daytime sleepiness.
142
Sleep medication use was assessed by one yes-no item: “In the past month, have you
143
taken any prescribed sleep medications (e.g., zolpidem (Ambien), zaleplon (Sonata), etc.)?
144
Please count only those you took, or are taking, several times per week.”
145
Assessment of exposure
146
Procrastination was assessed by 3 items from a previously psychometrically-validated
147
scale – the General Procrastination Scale, which assesses the global tendencies towards
148
chronic/habitual procrastination across a variety of daily tasks (Lay, 1986). In the 3-item
149
procrastination scale, respondents were asked how well the statements described themselves:
150
1) “I often find myself performing tasks that I had intended to do days before”, 2) “I generally
151
delay before starting on work I have to do”, and 3) “I am continually saying ‘I'll do it tomorrow’”.
152
Reponses to these items were given along a five-point Likert scale (extremely unlike me,
153
moderately unlike me, neutral, moderately like me, and extremely like me). A continuous
154
composite procrastination score was calculated by taking the mean of the items, with higher
155
values indicating higher levels of procrastination. The 3-item scale was estimated to have good
156
internal consistency reliability (Cronbach alpha = .86) in the 2011 HMS data. The variable was
157
centered at the sample mean in all regression analyses.
158
Covariates
159
Given that participants’ gender and age may determine both procrastination and sleep
160
behaviors (Steel, 2007; Wittmann et al., 2006), we controlled for these two variables to reduce 7
161
potential confounding bias. We also controlled for respondents’ sociodemographic and
162
academic characteristics to increase precision as they are known correlates of sleep (Ertel,
163
Berkman, & Buxton, 2011). Adjusted variables include race/ethnicity, relationship status, living
164
arrangement (with parents/guardians or not), past financial status, parental education (highest
165
level of education completed by mom or dad), academic level, and daily school work time.
166
Additionally, dummy variables of schools were included to account for systematic differences
167
that might affect students’ sleep such as school start times. In sensitivity analyses we also
168
adjusted for anxiety and depressive symptoms. Anxiety was assessed by the Patient Health
169
Questionnaire (PHQ) algorithm. Generalized anxiety was diagnosed if the subject had been
170
bothered by “feeling nervous, anxious, on edge, or worrying a lot about different things” for at
171
least more than half the days in the past 4 weeks and if 3 or more of the symptom criteria had
172
been present at least more than half the days (Spitzer, Kroenke, Williams, & Group, 1999).
173
Depressive symptoms were assessed by the PHQ-9, the depression module. Major depression
174
was diagnosed if 5 or more of the symptom criteria had been present at least more than half
175
the days in the past 2 weeks, and 1 of the symptoms was depressed mood or anhedonia
176
(Kroenke, Spitzer, & Williams, 2001).
177
Statistical analyses
178
For the main analyses, we ran simple and multiple survey linear regression models to
179
examine the associations of procrastination with social jetlag and sleep duration. Survey
180
Poisson regression models were used to evaluate the relationships between procrastination
181
with binary sleep quality indicators (insomnia symptoms, daytime sleepiness, and sleep
182
medication use) (Zou, 2004). To estimate 95% confidence intervals for the relative risks and
183
Wald-based p-values, we used the robust sandwich estimator of the variance. Sensitivity 8
184
analyses were conducted to assess if the results were influenced by data cleaning by excluding
185
observations with identified sleep timing data entry errors. We also conducted sensitivity
186
analyses by adjusting anxiety and depressive symptoms. Additionally, we assessed the
187
relationships between the sleep outcomes and procrastination based on quartiles of the
188
procrastination score. Inverse probability weighting were applied to all analyses using weights
189
provided by HMN to improve representativeness of the full student population in terms of the
190
available administrative variables (Seaman & White, 2013). All analyses were performed using STATA version 15.1 (StataCorp LP, College Station,
191 192
Texas). All tests were two-sided, with a significance level of 5%.
193
Results
194
Descriptive traits of the analytic sample are presented in Table 1. Slightly more than half
195
(58.0%) of the weighted sample were female. About three-quarters (78.6%) of the total were
196
between the ages 18 and 22. The racial/ethnic composition was 77.2% white, 7.0% black, 5.3%
197
Hispanic/Latino, 4.5% Asian, and 5.9% other. Approximately 1 in two (52.9%) were single; 9.9%
198
were living with parents or guardians. Few participants (2.7%) grew up in a very poor financial
199
situation without enough to get by, 29.7% enough to get by, about half (56.3%) comfortable,
200
and 11.3% well to do; 12.0% had a parent with high school education or less, 22.5% with an
201
associate’s degree or some college, 29.5% with a college degree, 36.1% with a graduate degree.
202
The majority (81.1%) were undergraduate students. Slightly more than one-third (39.8%) spent
203
2 hours or less per day on school work, 36.2% spent 3-4 hours, and 24.0% spent 5 hours or
204
more.
205 206
The mean of procrastination score was 3.3 (SD: 1.1) on a 1-5 scale (see Figure 1 for its distribution). The mean of social jetlag was 1.8 hours (SD: 1.0). Figure 2A presents its 9
207
distribution. About four-fifths (81.3%) of the sample suffered from at least one hour of social
208
jetlag. Figure 2B presents the relationship between social jetlag and age, highlighting that social
209
jetlag peaked at the end of adolescence and decreased afterwards. Respondents slept longer
210
on weekend nights (mean: 8.6 hours; SD: 1.4) than on weeknights (mean: 7.8 hours; SD: 1.5);
211
the mean of weekly average sleep duration was 8.1 hours (SD: 1.3) (see Figure 3 for
212
distributions of sleep duration). About one-fifth were bothered by insomnia symptoms, 31.6 %
213
daytime sleepiness, and 3.9% sleep medication use.
214
Table 2 reports results from multiple linear regression models separately estimating social
215
jetlag, mean weekly sleep duration, weeknight sleep duration, and weekend night sleep
216
duration (all assessed in minutes). Procrastination was positively linked to social jetlag (β = 3.34
217
min; 95% CI [1.86, 4.81]), indicating that procrastinators suffer more circadian misalignment
218
than non-procrastinators.
219
For sleep duration, higher procrastination scores were significantly associated with
220
shorter mean weekly sleep duration (β = -4.44 min; 95% CI [-6.36, -2.52]) and shorter weeknight
221
sleep duration (β = -6.10 min; 95% CI [-8.37, -3.84]), after controlling for sociodemographic and
222
academic factors. On the other hand, procrastination was not significantly associated with
223
weekend night sleep duration. Thus, the shorter mean weekly sleep duration experienced by
224
procrastinators were driven by their shorter weeknight sleep duration. Specifically, with a one-
225
unit increase in procrastination score, weeknight sleep duration decreased by 6 minutes,
226
resulting in a total 24 minutes sleep/night difference between the lowest and the highest levels
227
of procrastination per night. For mean weekly sleep duration, an average of 4 minutes less
228
sleep per night was associated with a one-unit increase in procrastination score, which was
229
approximately half an hour per week decrease in sleep. 10
230
Table 3 reports results from multiple Poisson regression models separately estimating
231
indices of sleep quality, including insomnia symptoms, daytime sleepiness, and sleep
232
medication use. Higher levels of procrastination were associated with elevated risks of
233
insomnia symptoms (Relative Risk (RR) = 1.27; 95% CI [1.19, 1.37]) and daytime sleepiness (RR =
234
1.32; 95% CI [1.27, 1.38]). However, there was no evidence that procrastination was associated
235
with sleep medication use.
236
In sensitivity analyses, we re-estimated all models restricting the sample to respondents
237
with no data entry errors to evaluate if data cleaning had an impact on the inferences, as well
238
as adjusting for anxiety and depressive symptoms. Results were similar to those from the main
239
analyses, which spoke to the robustness of our findings. Results based on quartiles of the
240
procrastination score were similar to those from the main analyses as well. Specifically,
241
compared to individuals in the lowest quartile of the procrastination score, those in the highest
242
quartile of the procrastination score had greater social jetlag (β = 10.98 min; 95% CI [6.30,
243
15.65]), shorter mean weekly sleep duration (β = -15.34 min; 95% CI [-21.45, -9.24]), and
244
shorter weeknight sleep duration (β = -20.52 min; 95% CI [-27.73, -13.31]) (Table A2). Those
245
who procrastinated the most also had greater odds of insomnia symptoms (RR = 2.06; 95% CI
246
[1.70, 2.51]) and daytime sleepiness (RR = 2.21; 95% CI [1.96, 2.49]) compared to those who
247
procrastinated the least (Table A3).
248
Discussion
249
This study investigated the relationships between self-reported procrastination and a
250
variety of sleep behaviors (social jetlag, sleep duration, insomnia symptoms, daytime sleepiness,
251
and sleep mediation use) among US undergraduate and graduate students. Procrastination was
252
positively associated with social jetlag, a delay in sleep timing from weeknights to weekend 11
253
nights, indicating that procrastinators experienced more circadian misalignment than non-
254
procrastinators. Second, higher levels of procrastination were associated with shorter mean
255
weekly sleep duration driven by shorter weeknight sleep duration experienced by
256
procrastinators. In contrast, weekend night sleep duration was not linked to procrastination.
257
Lastly, greater procrastination was also associated with increased risks of insomnia symptoms
258
and daytime sleepiness, but not sleep medication use.
259
While this study, guided by the Procrastination-Health Model, postulated that
260
procrastination might be a determinant of poor sleep. Some prior studies have, on the other
261
hand, argued that the causal direction could be the opposite (van Eerde & Venus, 2018).
262
Namely, sleep deprivation results in depleted self-control resources, which make the affected
263
individuals more likely to procrastinate (Kühnel, Bledow, & Feuerhahn, 2016). There might
264
indeed exist feedback loops between procrastination and sleep such that procrastination
265
compromises sleep, which in turn results in a reduction in self-regulation resources, which
266
further exacerbates the tendency to procrastinate. Yet, research has shown that chronic
267
procrastination is trait like in that there might be genetic underpinnings to chronic
268
procrastination measured by the General Procrastination Scale (Gustavson, Miyake, Hewitt, &
269
Friedman, 2014, 2015), making it conceptually difficult to argue that over a short period of time
270
that lack of sleep would turn into chronic/habitual procrastination.
271
Kroese and colleagues introduced the concept of bedtime procrastination, “failing to go
272
to bed at the intended time, while no external circumstances prevent a person from doing so”
273
(Kroese, De Ridder, et al., 2014; Kroese, Evers, et al., 2014). They found that bedtime
274
procrastination was linked to reports of insufficient sleep in an Amazon Mechanical Turk
275
sample (N = 177) and a Dutch adult sample (N = 2,431) (Kroese, De Ridder, et al., 2014; Kroese, 12
276
Evers, et al., 2014). However, these two studies addressed only one specific domain of
277
procrastination – bedtime procrastination, but not general procrastination. Two other studies
278
examined the link between general procrastination and sleep. Hairston surveyed 598
279
individuals and found that procrastination was related to insomnia symptoms (Hairston &
280
Shpitalni, 2016). Sirois and colleagues evaluated the problem in two samples, one of Greek
281
undergraduate students (N = 141) and one of Canadian students (N = 339). Results revealed
282
that procrastination was associated with indices of poor sleep quality and that higher stress
283
seemed to play a role in explaining the association (Sirois et al., 2015). Our finding that
284
procrastination was linked to insomnia symptoms and daytime sleepiness is consistent with
285
previous findings (Hairston & Shpitalni, 2016; Sirois et al., 2015). A prior study found an
286
association between procrastination and sleep medication use (Sirois et al., 2015) whereas we
287
did not. A reason for the difference could be that the question in the HMS survey was very strict
288
in asking respondents to only count “prescribed” sleep medications they took or were taking
289
“several times per week”, which could signal serious sleep disorders. In contrast, other surveys
290
usually ask respondents how often they took prescribed or over-the-counter sleep medications.
291
The fact that procrastination was only associated with weeknight but not weekend night
292
sleep duration could arise from different sleep patterns (times) on weeknights and weekend
293
nights based on levels of procrastination. Specifically, procrastinators had later sleep onsets
294
compared to non-procrastinators on both weeknights and weekend nights (observation from
295
the HMS data, summary available upon request). This is consistent with prior research showing
296
that procrastinators tend to be evening owls (Díaz-Morales, Ferrari, & Cohen, 2008; Digdon &
297
Howell, 2008). This resulted in shorter sleep duration on weeknights (because of early sleep
298
offsets – schools start at the same time regardless of students’ bedtime) and slightly longer 13
299
sleep duration on weekend nights (because of late sleep offsets thanks to fewer external
300
constraints on free days). Procrastinators’ shifted sleep times between weeknights and
301
weekend nights also explained the greater social jetlag they experienced: the difference
302
between their mid-sleep times on weekdays and weekends were bigger than that of non-
303
procrastinators’.
304
There are a few possible explanations for procrastinators’ late sleep onsets. First,
305
procrastinators might have to stay up late to finish the tasks they had postponed doing (thereby
306
displacing the hours available for sleep). Second, they might simply delay the action of going to
307
bed (bedtime procrastination (Kroese, De Ridder, et al., 2014)) by not quitting other activities
308
such as checking social media or devices, or other screen-based activities. The extra (primarily
309
blue) light exposure they receive (e.g., from using electronic devices) before bedtime may
310
additionally suppress melatonin levels (Chang, Aeschbach, Duffy, & Czeisler, 2015), which in
311
turn would delay sleep onsets.
312
One emerging sleep health component related to social jetlag is sleep regularity, which is
313
important for understanding sleep disorders such as insomnia and circadian rhythm disorders
314
(Buysse, 2014). Studies have linked sleep regularity to cardiometabolic outcomes – increased
315
variability in sleep duration and timing was associated with metabolic abnormalities (Huang &
316
Redline, 2019; Taylor et al., 2016). While social jetlag describes sleep timing differences
317
between weeknights and weekend nights, sleep regularity captures more general, night-to-
318
night variability in sleep patterns. Procrastination might be more strongly associated with night-
319
to-night sleep regularity than social jetlag, since sleep schedules are more likely to be
320
influenced by procrastination behaviors on a daily basis depending on whether there is an
14
321
urgent task. Although our data did not allow us to assess sleep regularity, future research might
322
consider investigating the relationship between procrastination and sleep regularity.
323
This study contributes to the literature in four ways. First, our study is the first that has
324
evaluated the relationship between procrastination and social jetlag in a large national student
325
sample. Second, this study is innovative in that it investigates the distinct associations between
326
procrastination and mean weekly sleep duration, weeknight sleep duration, and weekend night
327
sleep duration. Prior research has only investigated whether procrastination is linked to
328
average/typical sleep duration during the past month (Sirois et al., 2015) or just weekday sleep
329
duration (Kroese, De Ridder, et al., 2014). A differentiation between mean weekly, weeknight,
330
and weekend night sleep duration would elucidate the how procrastination is tied to sleep
331
duration on different types of days. Third, we investigate the relationships between
332
procrastination with indices of sleep quality, including insomnia symptoms, daytime sleepiness,
333
and sleep medication use. Combined with social jetlag and sleep duration, these different
334
aspects of sleep paint a comprehensive picture of sleep health. Fourth, this study focuses on US
335
adolescents and young adults, populations that suffer from serious problems of both
336
procrastination and sleep deprivation (Edens, 2006; Hill et al., 1978). The large national sample
337
used would afford generalizable insights on procrastination and sleep among US undergraduate
338
and graduate students.
339
This study has limitations. First, the cross-sectional nature of the data constrained our
340
ability to make causal inferences. Given the potential cyclic relationship between
341
procrastination and sleep, more studies with longitudinal or experimental designs are
342
warranted. Efforts could also be devoted to investigating the potential mediating roles of health
343
behaviors. Second, while applying the response propensity weights constructed by the HMN to 15
344
analyses increased our confidence that the weighted estimates were representative of the full
345
student population in terms of administrative variables, our results were not immune from
346
selection bias as it was possible that students who were procrastinators and whose sleep was
347
suboptimal were less likely to respond to the survey. Lastly, both procrastination and sleep
348
measures were self-reported by respondents in the HMS data, which might induce common-
349
method bias. Prior research has shown that self-reported sleep duration overestimated sleep
350
duration by about 1 hour when compared with actigraphy-measured total sleep time or
351
polysomnography-defined sleep time. The self-reported sleep only moderately correlated with
352
objectively measured sleep (Jackson, Patel, Jackson, Lutsey, & Redline, 2018). Furthermore,
353
procrastination might affect the accuracy of individuals’ self-reported sleep times and the
354
extent of over-reporting of sleep duration, or affect how individuals perceive their sleep quality,
355
which might result in differential classification of the sleep outcomes. If the inaccurate
356
reporting is non-differential with respect to procrastination, it would drive the associations
357
towards the null. However, if there is differential misclassification – e.g., procrastinators over-
358
estimate how much sleep they are getting compared to non-procrastinators, this would drive
359
associations away from the null (the actual associations might be stronger than what we
360
observed). It would be of interest for future research to use objective measures of sleep (e.g.,
361
longitudinal actigraphy data) to elucidate the relationships between procrastination and
362
multiple dimensions of sleep.
363
Although the magnitudes of the observed associations are modest, the effect sizes for the
364
sleep outcomes, when accumulated over an extended period of time for habitual
365
procrastinators, might have substantial implications on their health and cognitive performance.
366
For instance, the Sleep Continuity Theory posits that a period of 10 minutes of uninterrupted 16
367
sleep is required for restoration to take place (Downey & Bonnet, 1987). Experimental research
368
has shown that sleeping as brief as 10 minutes could improve subjective and objective alertness
369
and increase feelings of vigor and decrease fatigue (Lovato & Lack, 2010).
370
Conclusions
371
Our study showed that procrastination was associated with more social jetlag, shorter
372
sleep duration, and worse sleep quality among US undergraduate and graduate students. The
373
findings highlight that procrastination might be a vulnerability factor for poor health, but not
374
just something prevalent and relatively harmless. With the increasing prevalence and
375
availability of temptations (e.g., ubiquity of electronic devices and 24/7 entertainment industry),
376
people are facing more distractions than before and the problem of procrastination might
377
continue to increase (Kroese, Evers, et al., 2014; Steel, 2007). Findings from this study inform
378
potential targeted interventions to help adolescents and young adults prevent and manage
379
procrastination and to improve their sleep health, which in turn, could promote their overall
380
well-being (mental and physical health) and performance. Procrastination treatment programs
381
that improve people’s time management skills would be of value, as would recommendations
382
tailored to reduce procrastination on going to bed. For instance, by simply adjusting proximity
383
to temptations (e.g., keep the cell-phone out of the bedroom), procrastination could be
384
reduced. In addition, training programs targeting emotion regulation skills might also be of
385
value, as research has shown that training of emotion-focused strategies decreases
386
procrastination (Marcus Eckert, David D Ebert, Dirk Lehr, Bernhard Sieland, & Matthias Berking,
387
2016).
17
388
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Table 1. Descriptive traits of HMS cohort, N=8,742 % / Mean(SD) Socio-demographics Gender Female 58.0% Male 42.0% Age (y) 18-22 78.6% 23-25 8.6% 26-30 5.5% 31+ 7.3% Race/ethnicity White 77.2% Black 7.0% Hispanic/Latino 5.3% Asian 4.5% Other 5.9% Relationship status Single 52.9% In a relationship 37.0% Married 9.3% Divorced/widowed 0.7% Living with parents or guardians Yes 9.9% No 90.1% Past financial situation Very poor, not enough to get by 2.7% Enough to get by 29.7% Comfortable 56.3% Well to do 11.3% Parental education High school or less 12.0% Associate's degree or some college 22.5% College degree 29.5% Graduate degree 36.1% Response propensity weights applied.
% / Mean(SD) Academic factors Degree level Undergraduate Graduate Other School work time (h/day) <= 2 3-4 >= 5 Mental health factors Major depression Yes No
81.1% 12.1% 6.9% 39.8% 36.2% 24.0%
9.8% 90.2%
Generalized anxiety Yes No Procrastination Procrastination (1-5 scale) Sleep Social jetlag (h/week) Mean weekly sleep duration (h/day) Weeknight sleep duration (h/day) Weekend night sleep duration (h/day) Insomnia symptoms Yes No Daytime sleepiness Yes No Sleep medication use Yes No
5.9% 94.1% 3.32 (1.10) 1.76 (.99) 8.06 (1.26) 7.84 (1.46) 8.60 (1.40) 20.8% 79.2% 31.6% 68.4% 3.9% 96.1%
Table 2. Linear regression models estimating continuous sleep outcomes Social jetlag
Mean weekly duration
Weeknight duration
Weekend night duration
Beta [95% CI]
Beta [95% CI]
Beta [95% CI]
Beta [95% CI]
5.26 [3.75,6.78]***
-2.11 [-4.07,-0.15]*
-3.41 [-5.70,-1.12]**
1.13 [-0.98,3.25]
3.34 [1.86,4.81]***
-4.44 [-6.36,-2.52]***
-6.10 [-8.37,-3.84]***
-0.29 [-2.39,1.82]
Procrastination (centered) Unadjusted Adjusted 95% confidence intervals in brackets. +
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Adjusted analyses controlled for gender, age, race/ethnicity, relationship status, living arrangement, past financial status, parental education, academic level, daily school work time, and school dummies. Response propensity weights applied.
Table 3. Poisson regression models estimating binary sleep outcomes Insomnia symptoms
Daytime sleepiness
RR [95% CI]
RR [95% CI]
Sleep medication use RR [95% CI]
Procrastination (centered) Unadjusted
1.27 [1.18,1.36]***
1.27 [1.19,1.37]*** Adjusted Exponentiated coefficients; 95% confidence intervals in brackets. +
1.31 [1.26,1.37]***
1.09 [0.93,1.29]
1.32 [1.27,1.38]***
1.11 [0.95,1.29]
p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Adjusted analyses controlled for gender, age, race/ethnicity, relationship status, living arrangement, past financial status, parental education, academic level, daily school work time, and school dummies. Response propensity weights applied.
Figure 1. Distribution of procrastination score
Figure 2. Distribution of social jetlag (A) and average social jetlag as a function of age (B)
Figure 3. Distributions of sleep durations
Research highlights -
Procrastination is associated with multiple dimensions of sleep in adolescents and young adults.
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Procrastination is associated with greater social jetlag.
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Procrastination is associated with shorter sleep duration.
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Procrastination is associated with increased risk of insomnia symptoms and daytime sleepiness.
Title Do procrastinators get worse sleep? Cross-sectional study of US adolescents and young adults Authors Xiaoyu Li, ScDa,b; Orfeu M. Buxton, PhDa,b,c,d; Yongjoo Kim, ScDa; Sebastien Haneuse, PhDe; Ichiro Kawachi, MD, PhDa a
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public
Health, Boston, MA; bDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; cDepartment of Biobehavioral Health, Pennsylvania State University, University Park, PA; dDivision of Sleep Medicine, Harvard Medical School, Boston, MA; eDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA Ethics approval The study was approved by the institutional review boards at all participating schools and all participants gave informed consent. More information is available at http://healthymindsnetwork.org/. The secondary data analyses were reviewed and approved by the human subjects committee of the Harvard T.H. Chan School of Public Health. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study.