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Early to bed, early to rise! Sleep habits and academic performance in college students


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Prior studies have placed emphasis on the need for adequate total sleep time for student performance. We sought to investigate the relative importance of total sleep time compared to the timing of sleep and wakefulness for academic performance. We performed a questionnaire-based survey of college students in October 2007. The questionnaire gathered detailed information on sleep habits including naps, reasons for missing sleep, academic performance, study habits, time spent working outside of school, and stimulant use. Compared to those with the lowest academic performance, students with the highest performance had significantly earlier bedtimes (p = 0.05) and wake times (p = 0.008). Napping tended to be more common among high performers (p = 0.07). Of importance, there were no significant differences in total sleep time with or without naps, weekend sleep habits, study time, gender, race, reasons for staying up at night, nor in use of caffeinated beverages, over-the-counter stimulant pills, or use of prescription stimulants. Timing of sleep and wakefulness correlated more closely with academic performance than total sleep time and other relevant factors. These findings have important implications for programs intended to improve academic performance by targeting sleep habits of students.
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Early to bed, early to rise! Sleep habits and academic
performance in college students
Arne H. Eliasson &Christopher J. Lettieri &
Arn H. Eliasson
Received: 3 May 2009 / Revised: 13 June 2009 / Accepted: 24 June 2009 /Published online: 15 July 2009
#US Government 2009
Purpose Prior studies have placed emphasis on the need for
adequate total sleep time for student performance. We
sought to investigate the relative importance of total sleep
time compared to the timing of sleep and wakefulness for
academic performance.
Methods We performed a questionnaire-based survey of
college students in October 2007. The questionnaire
gathered detailed information on sleep habits including
naps, reasons for missing sleep, academic performance,
study habits, time spent working outside of school, and
stimulant use.
Results Compared to those with the lowest academic
performance, students with the highest performance had
significantly earlier bedtimes (p=0.05) and wake times (p=
0.008). Napping tended to be more common among high
performers (p=0.07). Of importance, there were no signif-
icant differences in total sleep time with or without naps,
weekend sleep habits, study time, gender, race, reasons for
staying up at night, nor in use of caffeinated beverages,
over-the-counter stimulant pills, or use of prescription
Conclusions Timing of sleep and wakefulness correlated
more closely with academic performance than total sleep
time and other relevant factors. These findings have
important implications for programs intended to improve
academic performance by targeting sleep habits of students.
Keywords Academic performance .Total sleep time .
Bed time .Wake time .Circadian rhythm
Habitual sleep patterns undergo substantial changes from
childhood to adolescence [1,2] and young adulthood [3].
These changes are characterized by progressive delay in the
sleep phase without a decrease in need for sleep (internal
factors) [4,5]. At the same time, schedules (external
factors) frequently require earlier wake times and lead to
shorter total sleep time (TST) [6,7]. Sleep debt accumu-
lated during the week often leads to prolonged sleep
periods or catch-up sleep on weekends causing severe
day-to-day irregularities of sleep patterns in adolescents and
young adults [8]. Insufficient sleep time, with associated
sleepiness, fatigue, and inattentiveness, has been identified
as a major cause of poor academic performance among high
school and college-aged students [911]. In fact, various
school districts have delayed their high school start times to
mitigate the effects of circadian sleep phase delay [12].
Even the US Congress has taken active interest in the issue
and has considered legislation known as ZstoAsto
encourage later school start times for adolescents [13].
The opinions expressed herein are those of the authors and should not
to be construed as official or as reflecting the policies of either the
Department of the Army or the Department of Defense.
A. H. Eliasson
Scholars Program, Montgomery College,
Rockville, MD 20850, USA
C. J. Lettieri :A. H. Eliasson
Pulmonary, Critical Care and Sleep Medicine,
Department of Medicine, Walter Reed Army Medical Center,
Washington, DC 20307-5001, USA
A. H. Eliasson (*)
Pulmonary Service, Walter Reed Army Medical Center,
12515 Davan Drive,
Silver Spring, MD 20904, USA
Sleep Breath (2010) 14:7175
DOI 10.1007/s11325-009-0282-2
While it is clear that insufficient sleep is a major factor
governing mood, alertness, concentration, learning, and
ultimately performance in the academic environment [14],
the precise role of sleep quantity versus the impact of
circadian rhythms on performance remains ill-defined. We
sought to clarify the roles of TST and circadian rhythm on
academic performance among young adults by surveying a
population of college students.
Study sample and setting
In October 2007, we conducted a questionnaire-based
survey of students at the main campus of Montgomery
College, a community college in a northern suburb of
Washington, DC with enrollment of 60,000 full- and part-
time students. The college is ethnically and culturally
diverse with student body composition of Caucasian
(40%), African-American (28%), Asian (16%), and His-
panic (16%). Age distribution data show 41% of students
are 20 years old or younger, 35% age 21 to 29, and 24%
age 30 or older. The student body is 55% female and 45%
Members of the teaching faculty distributed a two-page
questionnaire by hand to all students present in their classes
over a 2-day period. These faculty members informed their
students that participation was anonymous and voluntary.
Students were given the questionnaire to complete during
class time for expeditious collection. The classes incorpo-
rated a cross-section of curriculum offerings, such as:
biology, history, English, astronomy, and art. The number
of questionnaires distributed and collected was tallied by
the principle investigator. Approval of this research was
granted by the colleges Institutional Review Board.
Survey instrument
The principle investigator, three college professors, and two
physicians board-certified in sleep medicine constituted a
panel to develop the research questionnaire. Their mandate
was to limit the survey to two pages printed with 12-font
size. The survey instrument asked students about their sleep
habits (bed time, wake time, nap frequency and length,
week day versus weekend sleep) and how satisfied they
were with their sleep. The survey tool further queried about
daytime alertness versus grogginess and early-morning
versus late-night tendency. School performance was mea-
sured by self-reported grade point average (GPA), and
students were asked to report their usual amount of study
time (hours per day). Students also answered demographic
questions, recorded their employment time outside of
school (hours per week), and use of stimulants (caffeinated
drinks, energy drinks, caffeine pills, and prescription
The survey instrument was then administered to a pilot
group of students for feedback on question readability,
clarity, and question-and-answer format issues. Adjust-
ments in the survey instrument were implemented based
upon feedback from the pilot group leading to a two-page
tool with 29 questions that required approximately 5 min
for completion.
Data analysis
Continuous variables were analyzed using the Studentst
test. All tests were two-tailed, and pvalues of <0.05 were
assumedtorepresentstatistical significance. Data are
presented as mean ± standard deviation or as mean/median
with range as appropriate. All analyses were performed
using the Statistical Package for the Social Sciences 12.0
(SPSS Inc, Chicago, IL).
Of 170 questionnaires, 157 (92%) were returnedby 76 women
(48%) and 81 men (52%). The mean age of the surveyed
students was 22.4±6.8 years (median 20 years, range 17 to
69 years). Racial composition was 42% Caucasian, 18%
Hispanic, 15% African-American, 13% Asian, and 12% self-
designated as other. Mean bedtime on nights before class was
12:16A.M.(range9:00P. M . to 4:00A.M.);meanwaketime
7:37A.M.(range3:30A.M.to12:30P. M.), with nightly sleep
time of 7 h 23 min. Mean total sleep time (including naps)
hour later (1:12A.M.) and wake time 2.5 h later (9:58A.M.)
with nightly sleep time 8 h 43 min.
Among the whole group of students, a minority
(42%) expressed satisfaction with their sleep, and those
who were satisfied slept for an average of 47 min
longer each 24-h period. For all students, there was a
sizable majority admitting to feeling sleepy during the
day and more specifically feeling groggy during class.
Data for the highest quintile of performers by GPA were
compared with the data gathered on the lowest quintile of
performers (see Table 1). The racial composition of the
highest performing quintile of students and the lowest
quintile of performers were similar to the composition of
the whole group and were not different from each other.
While TST did not differ between groups, there was a
significant difference in the timing of sleep between high
and low academic performers. Specifically, those with
the highest GPA had earlier bed times (12:00A.M.versus
12:38A.M., p=0.05) and earlier wake times (7:13A.M.
72 Sleep Breath (2010) 14:7175
versus 8:02A.M., p=0.008) than those with the lowest
GPA. There were no statistically significant differences in
TST with and without naps, weekend sleep habits, or
study time between the high performers and the low
performers. High performers were more likely to take naps
regularly than low performers (52% versus 29%, p=0.07).
The percentages of students who acknowledged a morning
tendency and those who were satisfied with their sleep
were not statistically different between high and low
performers (see Table 2). However, low-performing
students were more likely to factor sleep into their class
schedules (p=0.04). No other variables were associated
with academic performance. Specifically, there were no
differences in reasons provided for staying up at night, use
of caffeinated beverages, use of over-the-counter stimulant
pills, or use of prescription stimulants.
Chief among the findings of this study is that timing of
sleep and wakefulness appears to be a more important
contributor to academic performance than total amount of
sleep. TST did not correlate with self-reported grade point
average, while earlier bed times and wake times did
correlate with higher grades.
Table 1 Demographic data, grades, sleep times, and study times
All students Lowest quintile (GPA <2.7) Highest quintile (GPA>3.5) pvalue
Age (years) 22.4 ±6.8 22.8± 7.6 23.2± 10.8 0.96
Gender (%male) 52 55 40 0.35
GPA out of 4.0 3.2± 0.5 2.6± 0.3 3.8± 0.2 <0.001
Bedtime before class
12:16A.M. (9:00 P.M.4:00A.M.) 12:38 A.M. (10:30P.M.3:00A.M.) 12:00A.M. (9:00 P.M.4:00A.M.) 0.05
Wake time before class
7:37A.M. (3:30 A.M.12:30 P.M.) 8:02A.M. (6:00A.M.12:00P.M.) 7:13A.M. (5:00 A.M.9:30A.M.) 0.008
Total sleep time before class 7 h 23 min 7 h 35 min 7 h 29 min 0.62
Bedtime with no class
1:12A.M. (9:00 P.M.10A.M.) 1:29 A.M. (10:00P.M.5:30A.M.) 12:51A.M. (9:00 P.M.5:30A.M.) 0.28
Wake time with no class
9:58A.M.(6A.M.4:30P.M.) 10:25A.M. (7:00A.M.2:00 P.M.) 9:27A.M. (7:00 A.M.2:00P.M.) 0.11
Total sleep time with no class 8 h 43 min 8 h 53 min 8 h 35 min 0.50
Naps (% yes) 42 29 52 0.07
Study time 2 h 48 min 2 h 23 min 2 h 47 min 0.40
pvalue derived from ttest comparison between values of lowest and highest quintiles of GPA
Table 2 Morning tendency, daytime symptoms, reasons for staying up at night, and stimulant use
All students Lowest quintile (GPA<2.7) Highest quintile (GPA>3.5) pvalue
Morning tendency (% yes) 25 29 40 0.35
Sleep affect class schedule? (% yes) 61 68 42 0.04
Satisfied with sleep (% yes) 42 35 45 0.50
Sleepy during the day (% yes) 62 65 53 0.90
Groggy during class (% yes) 75 81 78 0.97
Stay up for school (% yes) 53 52 55 0.80
Stay up for job (% yes) 30 32 35 0.79
Stay up to socialize (% yes) 54 52 45 0.62
Stay up for other reasons (% yes) 46 42 39 0.80
Job hours per week 19± 15 20± 15 16± 12 0.25
Caffeinated drinks per day 1.6 ± 1.6 1.5±1.6 1.3± 1.2 0.44
Stimulant pill use (% yes) 5 6 6 0.57
Use prescribed stimulant (% yes) 7 3 6 0.57
pvalue derived from ttest comparison between values of lowest and highest quintiles of GPA
Sleep Breath (2010) 14:7175 73
The lack of correlation of TST with grades mirrors
findings of a prior report using similar methods [15].
However, the published literature contains many studies
that have demonstrated the importance of total sleep time
for full enhancement of intellectual functioning as well as
student safety behind the wheel [16]. The concept that sleep
deprivation erodes performance, academic and otherwise, is
not in debate. The importance of the current findings is that
circadian factors or the synchrony effect [17] also plays a
major role in academic performance.
This synchrony effect should be factored into any
interventional program designed for sleep improvement as
a means to enhancing school performance. It has been
previously demonstrated that moving school start times
does have the salutary effect of increasing total sleep time
[18] and improving attendance, but improvements as
measured with grades may not follow as was shown in
the Minneapolis Public Schools Start Time Study [12].
( In fact, later
wake-up times may be associated with lower average
grades [19].
Compared to lower-performing students, higher-
performing students in our study had sleep onset almost
40 min earlier on average and awakened almost 50 min earlier
with no significant difference in TST. The importance of sleep
timing is particularly interesting since there does not appear to
be a congruent association of morning tendency with
academic performance in this cohort. These results suggest
that higher-performing students are able to find a way to shift
their sleep phase somewhat earlier than lower-performing
students despite the same degree of morning/evening prefer-
ence. It may be that the findings of the current study would be
more pronounced in sleep phase-delayed students attending
high school where class schedules are less flexible than they
are in college.
It is also interesting that higher-performing students
show a trend of napping more commonly than low
performers, though TST with naps is not statistically
different between groups. None of the other measured
variables serve to suggest a different interpretation. Specif-
ically, use of caffeinated beverages, over-the-counter
stimulants, and prescription stimulants were the same for
both groups. Equally interesting, gender, race, and total
study time did not predict GPA for this group of students.
The implications of the findings of the current study with
regard to the development of an educational program may
include the need for greater flexibility in the timing of
course offerings. Flexibility of class schedules, as opposed
to rigid schedule requirements, may allow students to find a
more natural sleepwake cycle and improve academic
performance. An institution interested in creating a sleep
improvement program may benefit from the use of the
questionnaire created for this study (available as online
supplement). Additionally, it may be useful to include
questions on majors, year of study, and individually
important information such as body mass index and sleep
quality. Such information may greatly enhance the under-
standing of specific sleep issues facing the college students.
Many of the other findings of the current study parallel
those reported in populations of similar ages and circum-
stances. The sleep habits of our student population lead to a
sleep debt during the school week followed by attempts to
catch up on weekends. Furthermore, our students sleep less
than recommended amounts and on average do not go to
sleep before midnight despite schedules that require waking
before a full nights sleep is accomplished. The prevalence
of stimulant pills and drug use is also similar to that
previously reported [20].
Limitations of this study include the use of self-reported
data for sleep times and school grades rather than objective
data measured by actigraphy and grades reported by the
college registrar. However, sleep survey results have been
shown to be as reliable as objective measures, at least in
studies involving adolescent-aged subjects [21]. Another
limitation is the use of a single question rather than a
validated questionnaire to determine subject tendency for
morningness and eveningness [22]. However, in order to
distribute a survey tool that would not require an inordinate
amount of class time, it was necessary to limit its length.
The importance of adequate amounts of sleep for peak
performance is not being questioned by the findings of this
study. Our data underscore the important contribution of
timing of sleep and wakefulness relative to the academic
schedule. To summarize, while it has been previously
shown that rest is an important contributor to performance,
circadian rhythm or an earlier habitual sleep period may be
more influential than TST with regard to academic
Acknowledgments The authors wish to thank Professor Sharon
Ward and Dr. Aram Hessami for their expertise in helping develop the
questionnaire. We also thank Dr. Shweta Sen for her administrative
guidance. We thank all three professors for their invaluable contribu-
tion with the distribution and collection of the questionnaire survey.
1. Karacan I, Anch M, Thornby JI, Okawa M, Williams RL (1975)
Longitudinal sleep patterns during pubertal growth: four-year
follow up. Pediatr Res 9:842846
2. Taylor DJ, Jenni OG, Acebo C, Carskadon MA (2005) Sleep
tendency during extended wakefulness: insights into adolescent
sleep regulation and behavior. J Sleep Res 14:239244
3. Ohayon MM, Roberts RE, Zulley J, Smirne S, Priest RG (2000)
Prevalence and patterns of problematic sleep among older
adolescents. J Am Acad Child Adolesc Psychiatry 39:15491556
4. Carskadon MA (1990) Patterns of sleep and sleepiness in
adolescents. Pediatrician 17:512
74 Sleep Breath (2010) 14:7175
5. Carskadon MA, Vieira C, Acebo C (1993) Association between
puberty and delayed phase preference. Sleep 16:258262
6. Mercer PW, Merritt SL, Cowell JM (1998) Differences in reported
sleep need among adolescents. J Adolesc Health 23:259263
7. Fukuda K, Ishihara K (2001) Age-related changes of sleeping
pattern during adolescence. Psychiatry Clin Neurosci 55:231232
8. Reid A, Maldonado CC, Baker FC (2002) Sleep behavior of
South African adolescents. Sleep 25:423427
9. Wolfson AR, Carskadon MA (1998) Sleep schedules and daytime
functioning in adolescents. Child Dev 69:875887
10. Beebe DW, Rose D, Amin R (2008) Effect of chronic sleep
restriction on adolescentslearning and brain activity in a
simulated classroom: a pilot study. Sleep 31:A77
11. Estrada A, Killgore WD, Rouse T, Balkin TF, Wildzunas RM
(2008) Total sleep time measured by actigraphy predicts academic
performance during military training. Sleep 31:A134
12. (last accessed 23 February
&task=view&id=337 (last accessed 13 June 2009)
14. Dinges DF, Pack F, Williams K, Gillen KA, Powell JW, Ott GE,
Aptowicz C, Pack AI (1997) Cumulative sleepiness, mood
disturbance, and psychomotor vigilance performance decrements
during a week of sleep restricted to 45 hours per night. Sleep
15. Eliasson A, Eliasson A, King J, Gould B, Eliasson A (2002)
Association of sleep and academic performance. Sleep Breath
16. Danner F, Phillips B (2008) Adolescent sleep, school start times,
and teen motor vehicle crashes. J Clin Sleep Med 4:533535
17. Goldstein D, Hahn CS, Hasher L, Wiprzycka UJ, Zelazo PD
(2007) Time of day, intellectual performance, and behavioral
problems in morning versus evening type adolescents: is there a
synchrony effect? Pers Indiv Differ 42:431440
18. Wolfson AR, Spaulding NL, Dandrow C, Baroni EM (2007)
Middle school start times: the importance of a good nights sleep
for young adolescents. Behav Sleep Med 5:194209
19. Trockel MT, Barnes MD, Egget DL (2000) Health-related
variables and academic performance among first-year college
students: implications for sleep and other behaviors. J Am Coll
Health 49:125131
20. Teter CJ, McCabe SE, Cranford JA, Boyd CJ, Guthrie SK (2005)
Prevalence and motives for illicit use of prescription stimulants in
an undergraduate student sample. J Am Coll Health 53:253262
21. Wolfson AR, Carskadon MA, Acebo C, Seifer R, Fallone G,
Labyak SE, Martin JL (2003) Evidence for the validity of a sleep
habits survey for adolescents. Sleep 26:213216
22. Cavallera GM, Giudici S (2008) Morningness and eveningness
personality: a survey in literature from 1995 up till 2006. Pers
Indiv Differ 44:321
Sleep Breath (2010) 14:7175 75
... Concurrently, previous research has suggested negative effects of poor sleep indices on academic performance [8,12,13]. Gaultney [8] sampled 1845 university students in America with a validated sleep disorder questionnaire. Results showed that 27% of students were at risk for at least one sleep disorder. ...
... Not all studies have reported the same relationship between sleep duration and academic performance. Eliasson (2010) suggested that bed times, sleep latency, and wake times had a greater impact on academic performance than sleep duration in 157 American university students [13]. Sweileh and colleagues (2011) also reported no relationship between sleep quality via the Pittsburgh Sleep Quality Index (PSQI) and academic success (via a self-reported 4-point scale) in 400 Palestinian undergraduate students [17]. ...
... The lack of findings for the relationship between academic grades and TST has also been reported previously. Eliasson (2010) reported that sleep factors such as bed and wake times had a greater impact on academic performance than TST in American university students [13]. Similar to the current study, when compared to lower-performing students, higher-performing students had an earlier sleep onset time with no significant difference in TST. ...
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This study aimed to determine the effect of sleep quantity and quality via the Pittsburgh Sleep Quality Index (PSQI) on students’ academic achievement in their first year of university study. In this cross-sectional study, 193 students (102 female, 91 male, mean ± SD; age = 19.3 ± 2.9 y) from an undergraduate Health degree in New Zealand completed the PSQI four weeks prior to the end of the semester in their first year of university study. Results from three core subjects in the first semester were averaged and correlations between the PSQI and academic success were evaluated using Spearman’s rho (ρ). The group were also trichotomized using a PSQI global score of ≤5 as the threshold for “good” sleepers (n = 62, 32%), a score of 5–8 for “moderate” sleepers (n = 63, 33%) and a score ≥8 to characterize “poor” sleepers (n = 68, 35%). Overall, students averaged 7 h 37 min of self-reported sleep duration with an average bedtime of 22:55 p.m. and wake time of 8:01 a.m. There was a significant, small inverse relationship between academic performance and bedtime (p = 0.03, ρ = −0.14), with those going to bed earlier having superior academic success. The trichotomized data demonstrated no significant differences in academic performance between students with poor, moderate and good sleep quality (p = 0.92). Later bedtimes were associated with lower academic performance in a group of first year university students. However, there were no other relationships observed between academic success and self-reported sleep quality or quantity as determined by the PSQI. Enhancing awareness of the impact of sleep timing on academic success should be prioritized and strategies to improve sleep hygiene should be promoted to university students.
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There are conflicting reports about the association between chronotype and academic achievement. Eveningness persons tend to have lower academic achievement, but have higher cognitive abilities. We hypothesized that sleep disturbance and daytime sleepiness, which are known to affect academic achievement, will interact with this association. To investigate the association, a sleep survey and covariance structure analysis was performed on high-school students. Among a total of 344 first-year high-school students, 294 students validly completed the questionnaire. The association between the recent change in their academic achievement, chronotype, daytime sleepiness, and sleep disturbance were analyzed. A simple comparison demonstrated that not chronotype but sleep disturbance and excessive daytime sleepiness were significant associated factors. Chronotype affects academic achievement through sleep disturbance and daytime sleepiness. Chronotype did not have a significant total effect on the reduction in academic achievement, whereas morningness had a significant direct effect and a significant indirect inverse effect through better sleep and less daytime sleepiness. This model accounted for 13.0% of the variance of the reduction in academic achievement. When discussing the association between chronotype and academic achievement, the effect of sleep disturbance and daytime sleepiness should be considered. Reducing sleep disturbance and daytime sleepiness with consideration to the chronotype of each person would be beneficial for the improvement of academic achievement.
University students are required to engage with new content and to be assessed at specific times of the day. Research has shown that circadian rhythms differ between individuals, with impacts on optimal functioning times. We investigate the extent to which deliberate, reflective thinking (critical for university level tasks) is impacted by the timing of tasks and the interaction of task timing with circadian rhythms. We use Cognitive Reflection Test (CRT) questions to assess students’ ability to use reflective thinking. By grouping students according to their diurnal preference (morning types or evening types), we either match or mismatch the timing of the CRT assessment with diurnal preference. We find that students experiencing circadian mismatch (morning types being assessed in the evening, or evening types being assessed in the morning) perform significantly worse on the CRT, suggesting less ability to invoke reflective thinking at times of circadian mismatch. This finding suggests that timing important assessments during the day, rather than in the early morning or evening, might improve performance of mismatched students.
Objective To investigate COVID-19’s impact on college student health behaviors. Participants 189 college students. Methods Participants completed an online survey on behaviors relating to sleep, sedentary activities, and physical activity before and during the COVID-19 pandemic. Comparisons utilized Students’ dependent t-test or Wilcoxon signed-rank tests. Results There was an increase in time to fall asleep (before: 23.4 ± 18.0 vs. during: 42.8 ± 44.3 min·day⁻¹, p < 0.001), time spent in bed (before: 7.8 ± 1.5 vs. during: 8.5 ± 1.5 hr·day⁻¹, p < 0.001), as well as shifts in later bed and awake time (p < 0.001). Total sedentary time increased during the pandemic (before: 9.0 ± 3.8 vs. during: 9.9 ± 4.1 hr·day⁻¹, p = 0.016); and time spent using a TV, computer, or phone (before: 3.1 ± 1.9 vs. during: 4.2 ± 2.3 hr·day⁻¹, p < 0.001). There was a significant decrease in moderate-vigorous activity (before: 123.8 ± 96.0 vs. during: 108.9 ± 75.5 min·week⁻¹, p = 0.028) and resistance training days (before: 2.4 ± 2.1 vs. during: 1.7 ± 2.1 days·week⁻¹, p < 0.001). Conclusions COVID-19 negatively influenced health behaviors in college students.
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To assess the effects of delayed high-school start times on sleep and motor vehicle crashes. The sleep habits and motor vehicle crash rates of adolescents from a single, large, county-wide, school district were assessed by questionnaire before and after a 1-hour delay in school start times. Average hours of nightly sleep increased and catch-up sleep on weekends decreased. Average crash rates for teen drivers in the study county in the 2 years after the change in school start time dropped 16.5%, compared with the 2 years prior to the change, whereas teen crash rates for the rest of the state increased 7.8% over the same time period. Later school start times may both increase the sleep of adolescents and decrease their risk of motor vehicle crashes.
To determine whether a cumulative sleep debt (in a range commonly experienced) would result in cumulative changes in measures of waking neurobehavioral alertness, 16 healthy young adults had their sleep restricted to an average 4.98 hrs per night for 7 consecutive nights. Ss slept in the laboratory, and sleep and waking were monitored. Three times each day, Ss were assessed for subjective sleepiness and mood and were evaluated on a brief performance battery that included psychomotor vigilance (PVT), probed memory (PRM), and serial-addition testing. Once each day they completed a series of visual analog scales (VASs) and reported sleepiness and somatic and cognitive/emotional problems. Sleep restriction resulted in statistically robust cumulative effects on waking functions. Subjective sleepiness ratings, subscale scores for fatigue, confusion, tension, and total mood disturbance from the mood and VAS ratings of mental exhaustion and stress were elevated across days of restricted sleep. PVT performance parameters were also significantly increased by restriction. Significant time-of-day effects were evident in subjective sleepiness and PVT data. Findings suggest that cumulative nocturnal sleep debt had a dynamic and escalating analog in cumulative daytime sleepiness and that asymptotic or steady-state sleepiness was not achieved in response to sleep restriction. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The article investigates central aspects of Morningness–Eveningness personality, focusing on recent literature in this field from 1995. A review was written by Kerkhof (1985) about interindividual differences in the human circadian system, where the author reviewed previous studies which had dealt with some aspects of Morningness–Eveningness personality (questionnaires, circadian rhythms, sleep-wake cycle, introversion–extraversion, age, sex, and the impact of disturbance) and a review was written by Tankova, Adan, and Buela-Casals (1994) about circadian typology and individual differences, where aspects of personality, age, sex, shift work, and life habits were examined. This paper, with reference also to previous studies, attempts to give an updating survey of some issues of the literature in the field of Morningness–Eveningness personality from 1995 up to 2006; various aspects of Morningness–Eveningness personality are taken into consideration: personality traits, biological, and genetic issues, Morningness–Eveningness preferences in Youngs and Adults, gender, cognitive abilities, work schedules, life habits, and cross-cultural research.
There were little or no indications of differences in sleep outcomes between the sexes. Results indicate a disturbance of sleep on initial laboratory nights relative to later nights. The results reported here clearly document the persistence of these effects from year to year. For the most part, sleep characteristics during the 4 years immediately after onset of puberty appear to represent a typical phase in the gradual patterns of changes across all ages. Total sleep time decreased markedly from 560 min in age range 10-12 to 424 min in age range 20-29, with our puberty subjects as intermediate levels. Puberty subjects has an average of 2.5 awakenings/night in the first 2 years as compared with 1.2/night in the last 2 years. The number of sleep stage shifts during the night varied around a constant mean value of approximately 37/night throughout all ages. The number of rapid eye movement (REM) period during the night decreased sharply for individuals from childhood (6.9/night) through adolescence (4.0/night), remaining constant thereafter. Percentages of the various sleep stages were fairly constant for individuals from age range 10-12 through age 30-39. Our puberty subjects had percentage profiles in near perfect agreement with the normal ontogenetic process. Normative data suggest that slow wave sleep reaches a peak at some point during the teen years.
Most studies of adolescent sleep habits show a pattern of decreasing total sleep time, a tendency to delay the timing of sleep, and an increased level of daytime sleepiness. Laboratory tests have shown that adolescents do not have a decreased need for sleep but probably need more sleep than prepubertally. A number of factors affect the development of adolescent sleep patterns. Puberty itself imposes a burden of increased daytime sleepiness with no change in nocturnal sleep. Parental involvement in setting bedtimes wanes, though they become increasingly involved in waking teenagers in the mornings. Curfews and school schedules also affect adolescent sleep patterns, seen most commonly as imposing earlier rise times as the school day begins earlier during the adolescent years. Part-time employment has a significant impact on the sleep patterns of teenagers: those who work more than 20 h each week sleep less, go to bed later, are more sleepy, and drink more caffeine and alcohol. Development of circadian rhythms may also play a role in the phase delay teenagers commonly experience. The primary conclusion is that many adolescents do not get enough sleep. The consequences of the chronic pattern of insufficient sleep are daytime sleepiness, vulnerability to catastrophic accidents, mood and behavior problems, increased vulnerability to drugs and alcohol, and development of major disorders of the sleep/wake cycle. Educational programs hold the promise of improving teenagers' sleep patterns through informing youngsters, parents, and pediatricians about proper sleep hygiene and the risks of poor sleep habits.
Many teenagers go to bed and wake up significantly later than younger children, a developmental progression thought to reflect adolescent psychosocial processes. To determine whether biological processes may underlie a delay of phase preference in adolescents, 183 sixth-grade boys and 275 sixth-grade girls completed questionnaires for morningness/eveningness (M/E) and pubertal status. School environment and birth order were also evaluated. A significant relationship of pubertal status to M/E was found in girls, with a similar though nonsignificant trend in boys. No relationship between M/E and psychosocial factors was found. These data support involvement of a biological factor in the adolescent phase preference delay and indicate that our current understanding of adolescent sleep patterns may need revision.
Sleep and waking behaviors change significantly during the adolescent years. The objective of this study was to describe the relation between adolescents' sleep/wake habits, characteristics of students (age, sex, school), and daytime functioning (mood, school performance, and behavior). A Sleep Habits Survey was administered in homeroom classes to 3,120 high school students at 4 public high schools from 3 Rhode Island school districts. Self-reported total sleep times (school and weekend nights) decreased by 40-50 min across ages 13-19, ps < .001. The sleep loss was due to increasingly later bedtimes, whereas rise times were more consistent across ages. Students who described themselves as struggling or failing school (C's, D's/F's) reported that on school nights they obtain about 25 min less sleep and go to bed an average of 40 min later than A and B students, ps < .001. In addition, students with worse grades reported greater weekend delays of sleep schedule than did those with better grades. Furthermore, this study examined a priori defined adequate sleep habit groups versus less than adequate sleep habit groups on their daytime functioning. Students in the short school-night total sleep group (< 6 hr 45 min) and/or large weekend bedtime delay group (> 120 min) reported increased daytime sleepiness, depressive mood, and sleep/wake behavior problems, ps < .05, versus those sleeping longer than 8 hr 15 min with less than 60 min weekend delay. Altogether, most of the adolescents surveyed do not get enough sleep, and their sleep loss interferes with daytime functioning.
Increased lifestyle demands and reduced sleep are reported to result in daytime sleepiness and impaired functioning for teenagers. A sample of 612 freshman urban high school students completed a questionnaire describing their sleep patterns and problems, along with sociodemographic information, daily activities, pubertal development, depressive mood, and morning-evening preference. About 63% of the respondents felt they needed more sleep on weeknights (MS group), experienced sleepiness that interfered with their schoolwork, and had problems with sleeping. The other group reported they got sufficient sleep on weeknights (SS group) and did not experience sleepiness problems to the same degree. However, both had similar weeknight sleep and daily activity patterns. The MS group reported an ideal sleep time of 9.2 h, about 2 h more than they were getting and 1 h more than the SS group ideal, and had a higher preference for later bed and waking times. In our sample, individual differences in biologic sleep need and quality of sleep may be emerging as early as 14 years of age.