Sleep and Health in Student Athletes
Sleep is an important part of overall health and functioning. Healthy sleep is important for cognitive
functioning, physical functioning, healing and recovery, and mental health. However, many student
athletes are not getting enough sleep at night, of sufficient quality. This study had 2 parts. First, a survey
was administered to 189 student athletes in order to better understand how sleep habits among student
athletes are related to mental and physical well-being. The second part consisted of a relatively simple
intervention, to see if this could improve student athletes’ sleep and whether it could also lead to
improvements in other areas of health and functioning.
The survey consisted of a number of standardized questionnaires that assessed sleep, mental health,
daytime functioning, and academic life. Overall, the study found that sleep problems are very common
among student athletes. For example, over 2/3 of student athletes meet criteria for “poor sleep quality,”
and 23% demonstrate excessively high levels of fatigue. Many student athletes are not getting enough
sleep, with 43% getting less than the recommended 7 hours of sleep for adults. Sleep disorder symptoms
are also common, with 29% reporting that it takes them at least 30 minutes to fall asleep, 32% and 12%
reporting symptoms consistent with mild and moderate-severe insomnia, respectively, and 12% with a
moderate to high risk level for sleep apnea. Drowsy driving, which is potentially life-threatening, was
reported by 17% of student athletes. Taken together, sleep problems are very common among student
athletes.
The survey also found that student athletes with sleep problems are more likely to have worse mental
health, worse physical health, worse academic and athletic performance, and worse social functioning.
Sleep problems were an important marker of many problems that could affect many areas of functioning.
Interestingly, the primary driver of sleep problems was not specific time demands. Although many
activities were perceived to interfere with sleep, it was not necessarily the number of hours engaged in
those activities that dictated impacts on sleep. Rather, it may be the level of stress or other pressures
associated with these activities that result in insomnia, which drives decreased sleep duration.
In addition to the survey, an intervention was tested. This intervention was largely based on education
and support. The education component consisted of a 2-hour workshop on sleep science basics, how to
recognize sleep problems, and ways to improve sleep. The support component came from 24/7 access to
peer support individuals and study staff. This approach allowed for an intervention that was low-cost,
could be disseminated to many students at once, and could be given to any individual, in any sport. In
general, the intervention sought to give individual students the knowledge and tools needed to make
healthy changes to their sleep, as well as the support needed to make those changes. In addition, sleep
tracking was initiated (with sleep diary and accelerometry), daily text message reminders were used to
keep students engaged, and a financial incentive (weekly drawing) was used to promote adherence.
Despite the simple nature of the intervention, observed improvements were detected in many areas of
functioning, including sleep quality and mental health. When students were asked about their experience
with the program, 100% reported some sleep benefit, as well as benefits in other areas of functioning
(particularly energy level, athletic performance, and mental health).
PROBLEM STATEMENT
Student athletes face many challenges to their ability to maintain healthy mental and physical well-being.
The college experience is associated with a number of physiologic, social, and environmental stressors
that can exert long-term effects on an individual’s future health and quality of life. In addition, unique
demands of student athletes, including academic, physical performance, scheduling, and travel demands,
all represent additional stressors that interact to produce in poorer outcomes.
LITERATURE REVIEW
Insufficient sleep duration and poor sleep quality have been increasingly identified as important risk
factors for physical and mental well-being, especially among young adults. For example, insufficient
and/or poor quality sleep is strongly associated with the development of cardiometabolic disease risk
factors1-4, obesity5-10, depression11-21, anxiety disorders22-25, alcohol use26-29, other substance use27, 30-32,
poorer academic performance33-36, and reduced mental well-being37, 38, in addition to poorer athletic
performance39-46.
Data from a recent PAC12 report shows that 2/3 of students indicated that lack of flexible time is the
hardest thing about being an athlete, more than academic work47. Further, students identified sleep as
the first thing that their athletic time commitments kept them from doing, 77% felt that they got less sleep
than non-athletes, and >50% indicated that they would use an extra hour to catch up on sleep. In 2015
data from the American College Health Association48, (1) 29% of student athletes report significant sleep
difficulties, (2) 40% reported getting enough sleep on only ≤3 nights, (3) 10% reported no problems with
daytime sleepiness, (4) >15% reported sleepiness to be at least “a big problem,” (5) >25% reported
extreme difficulty falling asleep ≥3 nights/week (consistent with a diagnosis of insomnia), and (6) >50%
reported that sleep problems impacted academic performance. Thus, student athletes frequently
experience sleep problems, they recognize the impact of sleep loss, and they are likely motivated to
improve sleep. These data are consistent with data from professional athletes, whose overall sleep quality
is poorer than non-athletes46.
Taken together, sleep is an aspect of health that is implicated in many of the most salient mental and
physical outcomes among college athletes. Despite this, obtaining sufficient sleep represents one of the
greatest challenges facing student athletes. No programmatic approaches have been able to address this
problem.
Accordingly, the proposed study aims to develop and assess the benefits of a novel sleep health
intervention targeted at student athletes. This intervention will be assessed with and without the
assistance of tools to aid in tracking sleep and adjusting to difficult schedules in order to evaluate the
utility of those approaches.
CONCEPTUAL FRAMEWORK
The proposed intervention is informed by well-supported models of health behavior change, including the
Health Belief Model, the Transtheoretical Model, and Behavioral Economics. Briefly, the Health Belief
Model49, 50 states that an individual will alter their behavior based on (1) perceived risk of maintaining
existing behavior, (2) perceived benefits of the new behavior, (3) perceived barriers in engaging in the
new behavior, (4) perceived ability to carry out the behavior, and (5) cues to action (reminders). The
Transtheoretical Model51-53, briefly, posits that behavior change occurs on a dimension of readiness to
change. Behavioral Economics54 theory, briefly, deals with the ways that complex health-promoting and
health-compromising behaviors may be supported by complementary reinforcers and weakened by
alternative reinforcers. Since many health-compromising behaviors have benefits that may vary across
individuals, financial incentives may act as a universal, simple, adaptable, alternative reinforcer55-64. As
such, several studies have documented the benefits of financial incentives to change relatively intractable
health-compromising behaviors55-58, 62, 63, 65.
METHODOLOGY AND DATA COLLECTION
Participants
Student athletes were recruited over the summer and through the first 2 weeks of the Fall semester, as
they arrived on campus. Recruitment was accomplished through flyers, referrals from athletics staff, and
in-person recruitment among athletes. To be eligible for the survey, students needed to be Division 1
athletes at least 18 years old (cheerleaders were also admitted). Recruitment targeted non-first-year
students, since they had not yet adjusted to college life and they would not be eligible for the intervention
phase of the study. All students who completed the survey were eligible for the intervention study,
though selection was targeted to those with sleep complaints.
Baseline Survey
A baseline survey was administered electronically. Students could complete the survey on their own
computer or mobile device or on a mobile device at the athletics facility. The survey consisted of the
measures:
Sleep and Fatigue. The Pittsburgh Sleep Quality Index (PSQI66) is a brief measure of overall sleep quality.
It includes items related to sleep duration, sleep disturbances, sleep efficiency, and daytime
consequences. A total score of 5 or above indicates overall poor sleep quality. The Insomnia Severity Index
(ISI67) is a 7-item screening questionnaire for insomnia symptoms. The ISI is a standard instrument for
clinical insomnia research68 and assessed total symptom severity. The Multivariable Apnea Prediction
Index (MAP69) is a screening tool for sleep apnea that provides a risk probability based on symptoms in
the context of other risk factors including age, sex, and body mass index. The Fatigue Severity Scale (FSS70)
is a brief measure of global fatigue and impact of fatigue on daytime functioning. The Circadian Energy
Scale (CIRENS) is a brief measure of morning and evening energy level71. The drowsy driving item from the
BRFSS72 was used to assess the prevalence of drowsy driving in the past month.
Stress and Mental Well-Being. The Perceived Stress Scale (PSS73) is a well-validated global measure of
stress. The GAD7 questionnaire74 is a 7-item screening tool for excessive anxiety and worry symptoms.
The Multidimensional Scale of Perceived Social Support (MSPSS) is a questionnaire that assesses global
levels of social support, with subscales for family, friends, and significant other75. A fourth subscale
(“team”) was added by changing the language of the items in the “friends” subscale to refer to teammates.
Overall well-being was assessed using the items from the Centers for Disease Control Health-Related
Quality of Life scale (HRQOL76). Mood was assessed with the Centers for Epidemiological Studies
Depression Scale77, a well-validated screening tool for subclinical and clinical mood symptoms.
Student Life. Students were asked how many hours per week they spend in the following activities: sportspecific practice or competition, working at a job, studying or doing school work, being in class, commuting
or traveling, and strength training and conditioning. They were asked to what degree these activities
interfered with sleep and mental well-being. Items from the National College Health Assessment were
used to assess whether sleep difficulties had an impact on academic performance.
Other Variables. Students were asked about frequency and timing of caffeine use and reasons for caffeine
use. They were also asked about smoking, alcohol use, and marijuana use. Students were asked their
height and weight (for BMI calculation). They were also asked for demographics including age, sex,
race/ethnicity, year in school, financial status (very poor, poor, lower middle class, middle class, upper
middle class, wealthy), living situation (campus residence hall, fraternity or sorority house, other
university housing, parent/guardian’s home, or other off-campus housing). Finally, they were asked to
estimate their overall grade range in school.
Intervention
The intervention recruited N=40 students from the survey sample and consisted of a 2-hour educational
session, provision of a FitBit tracking device, a 10-week period of sleep tracking using a sleep diary and
FitBit, entry into a weekly drawing, 24/7 access to peer support individuals and study personnel, daily text
messages, and a final survey. In addition, half of the participants (N=20) also received blue-blocking
glasses and a programmable light bulb (Up Light, Inc.) to potentially provide additional assistance in
regulating circadian rhythms.
Educational Session. The 2-hour education session was delivered by the PI and consisted of (1) a 10minute orientation to the intervention phase of the study, (2) a 20-minute overview of the basics of sleep,
how sleep is related to health, and how to understand and measure sleep, (3) a 20-minute overview of
different types of sleep problems, how they arise, and how to ameliorate them, (4) a 20-minute overview
of ways of preventing sleep problems and maximizing sleep ability, (5) a 10-minute overview of next steps,
study instructions, and ways to get support, and (6) a 30-minute question and answer period. A total of 4
sessions were held to accommodate students’ schedules.
FitBit tracking device. All participants were provided with a FitBit FLEX (FitBit Inc.) device. The FitBit FLEX
is a wrist-worn sleep and activity tracker. These devices are paired with a smartphone so that participants
can get daily feedback on their sleep (as measured using the device). Although there is some preliminary
sleep validation for devices such as these78, 79, it is not clear how accurate these devices are. The main
purpose of these devices are to provide daily feedback about sleep and serve as a physical reminder to
think about healthy sleep practices introduced in the educational session. Daily FitBit data was uploaded
into the Fitabase software, where it was then downloaded and analyzed.
10-week monitoring period. Participants wore the FitBit for 10 weeks. During this time, they also kept a
daily sleep diary, which was completed electronically (via computer and/or mobile device). The sleep diary
assessed time to bed, sleep latency, number of awakenings, wake time after sleep onset and wake time.
This also allowed for calculation of total time in bed, total sleep time, and sleep efficiency. Sleep diaries
also included general ratings of overall sleep quality and mood. Each day, subjects were contacted by
email and/or phone if no records were uploaded for that day for either the sleep diary or FitBit, they were
contacted by study staff and reminded to upload data.
Weekly drawing. A “regret lottery” was used to promote adherence to sleep tracking. This procedure
involves a drawing, where participants are entered into a drawing and notified if they were selected for a
prize; if they were adherent they are notified that they won and if they are not adherent, they are notified
that they would have won if only they were adherent. This type of lottery has been shown to be effective
for other health behaviors58. Each subject was given a randomly-selected 2-digit number. Each week, 2 2digit numbers were randomly generated. If the first digit matches the participant’s number, they receive
$10 if they are adherent. They receive $100 if both numbers match. Thus, each participant has a 2:10
chance to win $10 each week and 2:100 chance to win $100 each week.
Text messages. Subjects received daily text messages that serve as reminders about maintaining healthy
sleep habits. These text messages never repeated and consisted of several types of messages: (1)
reminders to adhere to sleep tracking, (2) reminders to adhere to healthy sleep practices, (3) tips for
improving sleep, and (4) fun facts about sleep. This variability was intended so that messages were not
overly repetitive and thus ignored.
Access to support. Two student athletes who completed the baseline survey (as well as additional peer
support training from the athletics department) were selected to be peer support individuals for the
study. When study participants had a question, they would send a text message to a pre-defined number.
This message would be automatically forwarded to both peer support students, as well as the study
coordinator and study email address. These messages could be sent 24/7 and responses were guaranteed
as quickly as possible. The peer support individuals would provide feedback and answer any questions
directly to the participant, under the guidance and supervision of the PI and study coordinator. Thus,
participants could ask any sleep-related question at any time and have access to the information they
need to make a healthy sleep decision.
Final survey. At the end of the study, the baseline survey was re-administered to all participants. In
addition, a feedback questionnaire was added, so that participants could evaluate their experiences with
the study. Participants were asked to evaluate the helpfulness of various components of the survey (0=10
scale, >5=“helpful” and >8=”very helpful”). These included the initial survey, learning about sleep science
in the information session, learning about sleep tips in the information session, getting text messages,
having 24/7 access to get questions answered, using a sleep diary, using a sleep tracker, getting access to
a weekly lottery, and (for those in the group that received them) the blue-blocking glasses and light bulb.
Participants were also asked whether they learned from the intervention, rated on a scale of 0-4.
Responses ≥3 (“a lot” or “very much”) were coded as “learned a lot.” These included the following: how
sleep is important to health, how sleep is important to daytime functioning, how sleep is important for
athletic performance, how sleep is important for mental well-being, how to tell if my sleep is good or bad,
how I actually sleep and how this varies over time, and how a good or bad night affects me the next day.
Participants were also asked how their sleep was changed as a result of this program, with items asking
whether they agree or disagree with statements, including: my sleep is better, I am more satisfied with
my sleep, I fall asleep easier, awakenings at night are less of a problem, my sleep timing is better, I know
what to do if I have trouble sleeping, I know what to do if I am sleepy during the day, and I am more
energized during the day. They were also asked if they agree or disagree that the program results in
improvements in stress, academic performance, athletic performance, social life, family life, mental
health, physical health, energy level, and ability to focus.
Blue-blocking glasses. Half of the participants were provided with Uvex blue-blocking glasses. These
glasses attenuate nearly all visible light in the blue-green spectrum. Since the circadian visual system relies
on this spectrum of light to inform the suprachiasmatic nucleus and shift circadian rhythms80-82, blocking
this frequency would prevent the circadian clock from receiving a “daytime” light signal. This would be
useful at night, when light exposure from electronic devices could artificially delay sleep onset83.
Programmable light bulb. Half of the participants were also given a special light bulb. This light bulb (Up
Light) fits in a regular light bulb socket but includes LED bulbs and electronics that allow for Bluetooth
connectivity (so the bulb can be controlled from a smartphone) and emit light in a wide range of colors
and brightness levels. So participants could program the bulbs to provide bright blue/green light in the
morning (to promote a “daytime” signal and potentially improve awakenings) and dim red light at night
(to avoid a “daytime” signal at night). These bulbs can also be set with alarms, so that it automatically
changes brightness and color based on the time of day. Participants were trained on the use of these
bulbs.
Statistical Analyses
For survey results, descriptive statistics were computer, in order to describe the sample. In addition,
regression analyses, adjusted for age, sex, and year in school, evaluated relationships between key
variables and poor sleep quality. For the pilot intervention, regression analyses, adjusted for age, sex, and
year in school examined changes in PSQI global score, sleep latency, bedtime, waketime, and sleep
duration, ISI score, ESS score, GAD7 score, CIRENS total and subscale scores, and drowsy driving. To
determine whether student time demands influenced sleep, regression analyses adjusted for sex and year
in school examined whether time spent in different activities and the degree to which students felt that
activities interfered with sleep were associated with sleep duration and quality. Analyses examining
interference of activities adjusted for time in that activity. Mean weekly sleep diary and sleep tracker data
were computed, and weeks 1 and 10 were compared using paired t-tests. To evaluate the benefit of the
added bulbs and blue blockers, regression analyses used sleep duration and quality change scores as
outcome and group as predictor. Also, students in the group that received these were asked whether they
believed that these were helpful, and descriptive statistics were calculated. Finally, perceived benefits of
the program were evaluated using descriptive statistics.
FINDINGS
Characteristics of the survey sample
Characteristics of the baseline survey are reported in Table 1. N=189 student athletes were recruited. The
sample consisted primarily of second-, third-, and fourth-year students.
Sleep quality and mental well-being
Poor sleep quality was associated with depression (B=1.14, p<0.0001), anxiety (B=0.79, p<0.0001), stress
(B=1.04, p<0.0001), fewer healthy days (B=1.03, p<0.0001), and less support from family (B=-0.31,
p=0.014), friends (B=-0.37, p=0.003), significant-other (B=-0.33, p=0.022), and teammates (B=-0.39,
p=0.001). Insomnia was associated with depression (B=0.85, p<0.0001), anxiety (B=0.50, p<0.0001), stress
(B=0.78, p<0.0001), fewer healthy days (B=0.60, p<0.0001), and less support from family (B=-0.30,
p<0.0001), friends (B=-0.28, p<0.0001), significant-other (B=-0.23, p=0.006), and teammates (B=-0.33,
p<0.0001). Fatigue was associated with depression (B=0.31, p<0.0001), anxiety (B=0.17, p<0.0001), stress
(B=0.24, p<0.0001), fewer healthy days (B=0.19, p<0.0001), and less support from family (B=-0.08,
p=0.018) and teammates (B=-0.10, p=0.003). Longer sleep duration was associated with less depression
(B=-1.85, p<0.0001), anxiety (B=-0.78, p=0.006), stress (B=-1.00, p=0.03), and more support from family
(B=0.93, p=0.005). To determine whether these relationships were simply explained by stress, PSS score
was entered as a covariate. In this case, nearly all relationships remained significant.
Sleep and time demands
Neither sleep duration nor insomnia were associated with hours spent in any activity. Shorter sleep
duration was seen among those who indicated that the following interfered with sleep: practice (B=0.43hrs, p=0.013), competition (B=-0.49hrs, p=0.007), training (B=-0.4hrs, p=0.012), and homework (B=0.56hrs, p=0.001). Higher ISI score was seen among those who indicated that the following interfered with
sleep: practice (B=3.86pts, p<0.0001), competition (B=3.68pts, p<0.0001), training (B=3.26pts, p<0.0001),
class (B=3.35pts, p<0.0001), and homework (B=4.07pts, p<0.0001). Mediation analyses showed that sleep
duration relationships were fully mediated by ISI score.
Intervention
Regarding changes in specific observed sleep variables obtained from questionnaires, the intervention
resulted in reduced sleep latency (12mins, p=0.0002), advanced waketime (32mins, p=0.033), lower PSQI
score (1.3pts, p=0.04), lower ISI score (3.5pts, p=0.0007), lower GAD score (1.6pts, p=0.025), decreased
drowsy driving (67%, p=0.009), increased morning energy (19%, p=0.05), increased evening energy (22%,
p=0.027), and increased total energy level (16%, p=0.019). No changes were seen for other variables.
Neither sleep duration nor insomnia were associated with hours spent in any activity. Shorter sleep
duration was seen among those who indicated that the following interfered with sleep: practice (B=0.43hrs, p=0.013), competition (B=-0.49hrs, p=0.007), training (B=-0.4hrs, p=0.012), and homework (B=0.56hrs, p=0.001). Higher ISI score was seen among those who indicated that the following interfered with
sleep: practice (B=3.86pts, p<0.0001), competition (B=3.68pts, p<0.0001), training (B=3.26pts, p<0.0001),
class (B=3.35pts, p<0.0001), and homework (B=4.07pts, p<0.0001). Mediation analyses showed that sleep
duration relationships were fully mediated by ISI score.
Weekly sleep diary and sleep tracker data are displayed in Table 2. No clear changes in mean weekly sleep
tracker data were observed. However, when the sample was restricted to the N=18 participants who wore
the device for both week 1 and week 10, significant changes were seen for total sleep time (31 additional
minutes, t(17)=-1.89, p<0.05), and time in bed (29.3 additional minutes, t(17)=-1.77, p<0.05).
Weekly sleep diary data are also presented in Table 2. Over the course of the intervention period, time in
bed and sleep time increased in general. This was reflected in a slightly later bedtime and wake time.
Number of awakenings and time awake after sleep onset nominally decreased, as did time awake in bed
in the morning. Sleep efficiency nominally increased by about 8%.
Regarding perceived benefits of the program, most participants reported benefits of the intervention.
Regarding specific statements, 83% agreed with the statement “I sleep better.” Also, agreement was high
for other statements, including, “I am more satisfied with my sleep” (83%), “I fall asleep easier” (77%),
“Awakenings at night are less of a problem” (74%), “My sleep timing is better” (91%), “I know what to do
if I have trouble sleeping” (86%), and “I know what to do if I am sleepy during the day” (97%). The percent
that agreed that they experienced positive changes in the following are reported: “Stress” (66%),
“Academic performance” (77%). “Athletic performance” (89%), “Social Life” (77%), “Family Life” (71%),
“Mental Health” (77%), “Physical Health” (86%), “Energy Level” (91%), and “Ability to Focus” (83%). Of
these 16 domains, improvement was reported in M=13.2 (SD=3.3) domains.
Glasses and Light Bulb
Regression analyses did not show that being in the group that had the glasses and light bulb produced
significantly different changes in sleep variables. When individuals in this group were asked to rate, on a
1-10 scale, how helpful these were for improving their sleep and functioning, the mean rating was 7.1
(SD=2.6, range 0-10) for glasses and 6.4 for the light bulb (SD=4.0, range 0-10). Of these, 50% rated the
glasses very helpful (score ≥ 8) and 50% did the same for the bulb though only 21% rated both highly.
Program feedback
Perceived benefits of the program are reported in Table 3. Overall, the majority of participants reported
that the components were helpful and that they benefitted from the program. Regarding components of
the program, the most popular were the information session (both science and tips) and the lottery. The
least popular were daily text messages and the sleep diary. Regarding what was learned, over 85%
reported that they learned a lot about how sleep is important for daytime functioning, athletic
performance, and mental health. Regarding changes to sleep, 100% of participants reported at least some
improvement in sleep; over 85% of respondents reported that their energy level and sleep timing is better,
that they have a better idea of what to do if they are having sleep troubles. Regarding changes in other
domains, all participants reported at least some benefit in other areas, with nearly all (>85%) reporting
improvements to physical health, energy level, and athletic performance.
IMPLICATIONS FOR CAMPUS LEVEL PROGRAMMING
The results of this pilot study have several implications for campus-level programming. These regard the
baseline survey, as well as the pilot intervention.
The baseline survey documents, using validated sleep instruments in a wide range of athletes, that sleep
problems are common among student athletes. In particular, the high rates of poor sleep quality (68%),
long sleep latency (29%), short sleep duration (43%), both mild (32%) and moderate-severe (12%)
insomnia, moderate-high probability of sleep apnea (12%), and excessive fatigue (23%) are informative
for planning campus-based sleep health promotion campaigns. In addition, the baseline survey
demonstrated that sleep in related to important areas of mental and physical well-being.
The intervention assessed as part of this study included several elements. Overall, the intervention was
well-received even though it mainly consisted of education and support, relying on athletes to apply what
they had learned to their own experiences. The strength of this approach is that it is easily disseminated
to a large group and is not dependent on the schedules associated with a particular team or sport. The
primary limitation with this approach is that the intervention does not employ more intensive programs
aimed at specifically-identified problems. For example, students identified as having difficulty relaxing are
not triaged into relaxation training. It is possible that a more complex and complete intervention would
produce greater results.
The relative success of this program, despite its simplicity, suggests that such a program could be readily
adapted for use by a number of athletics organizations at a relatively low cost. That the program produced
benefits in many areas of function, including sleep as well as both mental health and athletic performance,
supports the hypothesis that healthy sleep in athletes is an important part of overall functioning across
many domains. Future on-campus programs may explore modifications of this approach, including ondemand educational content, increased feedback to participants, and decreased burden. The financial
incentive component could also potentially be incorporated into a campus program.
Regarding future research directions, the results of this study suggest several avenues. First, more
intensive interventions should be explored, targeting high-risk individuals. Second, objective measures of
physical and athletic performance should be assessed relative to healthy sleep. Third, adaptations of this
approach to other on-campus groups, as well as the community at large, may demonstrate utility for
improving sleep health in other groups.
REFERENCES
1.
Grandner MA, Chakravorty S, Perlis ML, Oliver L, Gurubhagavatula I. Habitual sleep duration
associated with self-reported and objectively determined cardiometabolic risk factors. Sleep Med.
2014;15(1):42-50. PubMed PMID: 24333222.
2.
Grandner MA. Addressing sleep disturbances: An opportunity to prevent cardiometabolic
disease? International review of psychiatry. 2014;26(2):155-76. PubMed PMID: 24892892.
3.
Azadbakht L, Kelishadi R, Khodarahmi M, Qorbani M, Heshmat R, Motlagh ME, Taslimi M, Ardalan
G. The association of sleep duration and cardiometabolic risk factors in a national sample of children and
adolescents: the CASPIAN III study. Nutrition. 2013;29(9):1133-41. PubMed PMID: 23927946.
4.
Countryman AJ, Saab PG, Llabre MM, Penedo FJ, McCalla JR, Schneiderman N. Cardiometabolic
risk in adolescents: associations with physical activity, fitness, and sleep. Ann Behav Med. 2013;45(1):12131. PubMed PMID: 23080394.
5.
Araujo J, Severo M, Ramos E. Sleep duration and adiposity during adolescence. Pediatrics.
2012;130(5):e1146-54. PubMed PMID: 23027175.
6.
Lowry R, Eaton DK, Foti K, McKnight-Eily L, Perry G, Galuska DA. Association of Sleep Duration with
Obesity among US High School Students. J Obes. 2012;2012:476914. PubMed PMID: 22530111; PMCID:
3306918.
7.
Park S. Association between short sleep duration and obesity among South korean adolescents.
West J Nurs Res. 2011;33(2):207-23. Epub 2010/08/26. PubMed PMID: 20736380.
8.
Hart CN, Cairns A, Jelalian E. Sleep and obesity in children and adolescents. Pediatr Clin North Am.
2011;58(3):715-33. PubMed PMID: 21600351; PMCID: 3107702.
9.
Currie A, Cappuccio FP. Sleep in children and adolescents: a worrying scenario: can we understand
the sleep deprivation-obesity epidemic? Nutr Metab Cardiovasc Dis. 2007;17(3):230-2. Epub 2007/02/27.
PubMed PMID: 17321728.
10.
Yu Y, Lu BS, Wang B, Wang H, Yang J, Li Z, Wang L, Liu X, Tang G, Xing H, Xu X, Zee PC, Wang X.
Short sleep duration and adiposity in Chinese adolescents. Sleep. 2007;30(12):1688-97. Epub 2008/02/06.
PubMed PMID: 18246978; PMCID: 2276131.
11.
Franic T, Kralj Z, Marcinko D, Knez R, Kardum G. Suicidal ideations and sleep-related problems in
early adolescence. Early intervention in psychiatry. 2014;8(2):155-62. PubMed PMID: 23445244.
12.
Gregory AM, Sadeh A. Sleep, emotional and behavioral difficulties in children and adolescents.
Sleep Med Rev. 2012;16(2):129-36. Epub 2011/06/17. PubMed PMID: 21676633.
13.
Lee YJ, Cho SJ, Cho IH, Kim SJ. Insufficient sleep and suicidality in adolescents. Sleep.
2012;35(4):455-60. PubMed PMID: 22467982; PMCID: 3296786.
14.
Kalak N, Gerber M, Kirov R, Mikoteit T, Puhse U, Holsboer-Trachsler E, Brand S. The relation of
objective sleep patterns, depressive symptoms, and sleep disturbances in adolescent children and their
parents: a sleep-EEG study with 47 families. J Psychiatr Res. 2012;46(10):1374-82. PubMed PMID:
22841346.
15.
Coulombe JA, Reid GJ, Boyle MH, Racine Y. Sleep problems, tiredness, and psychological
symptoms among healthy adolescents. J Pediatr Psychol. 2011;36(1):25-35. Epub 2010/04/28. PubMed
PMID: 20421201.
16.
Pallesen S, Saxvig IW, Molde H, Sorensen E, Wilhelmsen-Langeland A, Bjorvatn B. Brief report:
behaviorally induced insufficient sleep syndrome in older adolescents: prevalence and correlates. J
Adolesc. 2011;34(2):391-5. Epub 2010/03/23. PubMed PMID: 20303581.
17.
Clinkinbeard SS, Simi P, Evans MK, Anderson AL. Sleep and delinquency: does the amount of sleep
matter? J Youth Adolesc. 2011;40(7):916-30. Epub 2010/10/12. PubMed PMID: 20936500.
18.
Baglioni C, Regen W, Teghen A, Spiegelhalder K, Feige B, Nissen C, Riemann D. Sleep changes in
the disorder of insomnia: a meta-analysis of polysomnographic studies. Sleep Med Rev. 2014;18(3):195213. PubMed PMID: 23809904.
19.
Baglioni C, Riemann D. Is chronic insomnia a precursor to major depression? Epidemiological and
biological findings. Curr Psychiatry Rep. 2012;14(5):511-8. PubMed PMID: 22865155.
20.
Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, Lombardo C, Riemann
D. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological
studies. J Affect Disord. 2011;135(1-3):10-9. PubMed PMID: 21300408.
21.
Baglioni C, Spiegelhalder K, Lombardo C, Riemann D. Sleep and emotions: a focus on insomnia.
Sleep Med Rev. 2010;14(4):227-38. Epub 2010/02/09. PubMed PMID: 20137989.
22.
Hall Brown T, Mellman TA. The influence of PTSD, sleep fears, and neighborhood stress on
insomnia and short sleep duration in urban, young adult, African Americans. Behav Sleep Med.
2014;12(3):198-206. PubMed PMID: 23767868; PMCID: 3966964.
23.
Gehrman P, Seelig AD, Jacobson IG, Boyko EJ, Hooper TI, Gackstetter GD, Ulmer CS, Smith TC.
Predeployment Sleep Duration and Insomnia Symptoms as Risk Factors for New-Onset Mental Health
Disorders Following Military Deployment. Sleep. 2013;36(7):1009-18. PubMed PMID: 23814337; PMCID:
3669076.
24.
Schoenfeld FB, Deviva JC, Manber R. Treatment of sleep disturbances in posttraumatic stress
disorder: a review. J Rehabil Res Dev. 2012;49(5):729-52. PubMed PMID: 23015583.
25.
Todd J, Mullan B. The role of self-regulation in predicting sleep hygiene in university students.
Psychol Health Med. 2013;18(3):275-88. PubMed PMID: 22788412.
26.
Saxvig IW, Pallesen S, Wilhelmsen-Langeland A, Molde H, Bjorvatn B. Prevalence and correlates
of delayed sleep phase in high school students. Sleep Med. 2012;13(2):193-9. Epub 2011/12/14. PubMed
PMID: 22153780.
27.
Fakier N, Wild LG. Associations among sleep problems, learning difficulties and substance use in
adolescence. J Adolesc. 2011;34(4):717-26. Epub 2010/10/19. PubMed PMID: 20952052.
28.
Yen CF, King BH, Tang TC. The association between short and long nocturnal sleep durations and
risky behaviours and the moderating factors in Taiwanese adolescents. Psychiatry Res. 2010;179(1):6974. Epub 2010/05/18. PubMed PMID: 20472300.
29.
Roane BM, Taylor DJ. Adolescent insomnia as a risk factor for early adult depression and substance
abuse. Sleep. 2008;31(10):1351-6. PubMed PMID: 18853932; PMCID: 2572740.
30.
James JE, Kristjansson AL, Sigfusdottir ID. Adolescent substance use, sleep, and academic
achievement: evidence of harm due to caffeine. J Adolesc. 2011;34(4):665-73. Epub 2010/10/26. PubMed
PMID: 20970177.
31.
Cohen-Zion M, Drummond SP, Padula CB, Winward J, Kanady J, Medina KL, Tapert SF. Sleep
architecture in adolescent marijuana and alcohol users during acute and extended abstinence. Addict
Behav. 2009;34(11):976-9. Epub 2009/06/10. PubMed PMID: 19505769; PMCID: 2727851.
32.
Gromov I, Gromov D. Sleep and substance use and abuse in adolescents. Child Adolesc Psychiatr
Clin N Am. 2009;18(4):929-46. Epub 2009/10/20. PubMed PMID: 19836697.
33.
Baert S, Omey E, Verhaest D, Vermier A. Mister sandman, bring me good marks! On the
relationship between sleep quality and academic achievement. Bonn, Germany: IZA (Institute for the
Study of Labor), 2014 June, 2014. Report No.: Contract No.: 8232.
34.
Gillen-O'Neel C, Huynh VW, Fuligni AJ. To study or to sleep? The academic costs of extra studying
at the expense of sleep. Child Dev. 2013;84(1):133-42. PubMed PMID: 22906052.
35.
de Carvalho LB, do Prado LB, Ferrreira VR, da Rocha Figueiredo MB, Jung A, de Morais JF, do Prado
GF. Symptoms of sleep disorders and objective academic performance. Sleep Med. 2013;14(9):872-6.
PubMed PMID: 23831238.
36.
Genzel L, Ahrberg K, Roselli C, Niedermaier S, Steiger A, Dresler M, Roenneberg T. Sleep timing is
more important than sleep length or quality for medical school performance. Chronobiol Int.
2013;30(6):766-71. PubMed PMID: 23750895.
37.
Mukherjee S, Patel SR, Kales SN, Ayas NT, Strohl KP, Gozal D, Malhotra A, American Thoracic
Society ad hoc Committee on Healthy S. An Official American Thoracic Society Statement: The Importance
of Healthy Sleep. Recommendations and Future Priorities. Am J Respir Crit Care Med. 2015;191(12):14508. PubMed PMID: 26075423.
38.
Chen X, Gelaye B, Williams MA. Sleep characteristics and health-related quality of life among a
national sample of American young adults: assessment of possible health disparities. Qual Life Res.
2014;23(2):613-25. PubMed PMID: 23860850; PMCID: 4015621.
39.
Juliff LE, Halson SL, Peiffer JJ. Understanding sleep disturbance in athletes prior to important
competitions. J Sci Med Sport. 2014. PubMed PMID: 24629327.
40.
Dettl MG. Do college athletes differ from colege nonathletes in their sleep quality? Athens, OH:
College of Health Sciences and Professions of Ohio University; 2013.
41.
Silva A, Queiroz SS, Winckler C, Vital R, Sousa RA, Fagundes V, Tufik S, de Mello MT. Sleep quality
evaluation, chronotype, sleepiness and anxiety of Paralympic Brazilian athletes: Beijing 2008 Paralympic
Games. Br J Sports Med. 2012;46(2):150-4. Epub 2010/12/22. PubMed PMID: 21173008.
42.
Leeder J, Glaister M, Pizzoferro K, Dawson J, Pedlar C. Sleep duration and quality in elite athletes
measured using wristwatch actigraphy. J Sports Sci. 2012;30(6):541-5. Epub 2012/02/15. PubMed PMID:
22329779.
43.
Taheri M, Arabameri E. The effect of sleep deprivation on choice reaction time and anaerobic
power of college student athletes. Asian J Sports Med. 2012;3(1):15-20. Epub 2012/03/31. PubMed PMID:
22461961; PMCID: 3307962.
44.
Mah CD, Mah KE, Kezirian EJ, Dement WC. The effects of sleep extension on the athletic
performance of collegiate basketball players. Sleep. 2011;34(7):943-50. Epub 2011/07/07. PubMed PMID:
21731144; PMCID: 3119836.
45.
Oliver SJ, Costa RJ, Laing SJ, Bilzon JL, Walsh NP. One night of sleep deprivation decreases treadmill
endurance performance. Eur J Appl Physiol. 2009;107(2):155-61. Epub 2009/06/23. PubMed PMID:
19543909.
46.
Reilly T, Edwards B. Altered sleep-wake cycles and physical performance in athletes. Physiol
Behav. 2007;90(2-3):274-84. PubMed PMID: 17067642.
47.
Penn Schoen Berland. Student-Athlete Time Demands: Penn Schoen Berland; 2015.
48.
American College Health Association. ACHA-NCHA II Spring 2015 Reference Group Data Report.
Hanover, MD: ACHA; 2015.
49.
Rosenstock IM. Why people use health services. Milbank Mem Fund Q. 1966;44(3):Suppl:94-127.
Epub 1966/07/01. PubMed PMID: 5967464.
50.
Champion VL, Skinner CS. The health belief model. In: Glanz K, Rimer BK, Viswanath K, editors.
Health Behavior and Health Education: Theory, Research, and Practice. San Francisco: Jossey-Bass; 2008.
p. 45-65.
51.
Prochaska JO, Johnson S, Lee P. The transtheoretical model of behavior change. In: Shumaker SA,
Ockene JK, Riekert KA, editors. Handbook of Health Behavior Change. 3rd ed. NY: Springer; 2009. p. 5983.
52.
Prochaska JO, Redding CA, Evers KE. The transtheoretical model and stages of change. In: Glanz
K, Rimer BK, Viswanath K, editors. Health Behavior and Health Education: Theory, Research, and Practice.
San Francisco: Jossey-Bass; 2008. p. 97-121.
53.
Prochaska JO, DiClemente CC. Transtheoretical therapy: Toward a more integrative model of
change. Psychotherapy: Theory, Research and Practice. 1982;19(3):276-88.
54.
Hursh SR. Behavioral economics. Journal of the experimental analysis of behavior.
1984;42(3):435-52. PubMed PMID: 16812401; PMCID: 1348114.
55.
Kullgren JT, Troxel AB, Loewenstein G, Asch DA, Norton LA, Wesby L, Tao Y, Zhu J, Volpp KG.
Individual- versus group-based financial incentives for weight loss: a randomized, controlled trial. Ann
Intern Med. 2013;158(7):505-14. PubMed PMID: 23546562.
56.
Kim AE, Towers A, Renaud J, Zhu J, Shea JA, Galvin R, Volpp KG. Application of the RE-AIM
framework to evaluate the impact of a worksite-based financial incentive intervention for smoking
cessation. J Occup Environ Med. 2012;54(5):610-4. PubMed PMID: 22476113.
57.
John LK, Loewenstein G, Troxel AB, Norton L, Fassbender JE, Volpp KG. Financial incentives for
extended weight loss: a randomized, controlled trial. J Gen Intern Med. 2011;26(6):621-6. PubMed PMID:
21249462; PMCID: 3101962.
58.
Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G. Financial incentive-based
approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631-7. PubMed PMID: 19066383;
PMCID: 3583583.
59.
Loewenstein G, Asch DA, Volpp KG. Behavioral economics holds potential to deliver better results
for patients, insurers, and employers. Health Aff (Millwood). 2013;32(7):1244-50. PubMed PMID:
23836740.
60.
Shea JA, Weissman A, McKinney S, Silber JH, Volpp KG. Internal medicine trainees' views of
training adequacy and duty hours restrictions in 2009. Acad Med. 2012;87(7):889-94. PubMed PMID:
22622211; PMCID: 3386471.
61.
John LK, Loewenstein G, Volpp KG. Empirical observations on longer-term use of incentives for
weight loss. Prev Med. 2012;55 Suppl:S68-74. PubMed PMID: 22342291.
62.
Kimmel SE, Troxel AB, Loewenstein G, Brensinger CM, Jaskowiak J, Doshi JA, Laskin M, Volpp K.
Randomized trial of lottery-based incentives to improve warfarin adherence. Am Heart J.
2012;164(2):268-74. PubMed PMID: 22877814.
63.
Halpern SD, Kohn R, Dornbrand-Lo A, Metkus T, Asch DA, Volpp KG. Lottery-based versus fixed
incentives to increase clinicians' response to surveys. Health Serv Res. 2011;46(5):1663-74. PubMed
PMID: 21492159; PMCID: 3207198.
64.
Volpp KG, Asch DA, Galvin R, Loewenstein G. Redesigning employee health incentives--lessons
from behavioral economics. N Engl J Med. 2011;365(5):388-90. PubMed PMID: 21812669; PMCID:
3696722.
65.
Cahill K, Perera R. Competitions and incentives for smoking cessation. Cochrane Database Syst
Rev. 2011(4):CD004307. PubMed PMID: 21491388.
66.
Buysse DJ, Reynolds CF, 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index:
a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213. PubMed
PMID: 2748771.
67.
Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome
measure for insomnia research. Sleep Med. 2001;2(4):297-307. Epub 2001/07/05. PubMed PMID:
11438246.
68.
Buysse DJ, Ancoli-Israel S, Edinger JD, Lichstein KL, Morin CM. Recommendations for a standard
research assessment of insomnia. Sleep. 2006;29(9):1155-73. Epub 2006/10/17. PubMed PMID:
17040003.
69.
Maislin G, Pack AI, Kribbs NB, Smith PL, Schwartz AR, Kline LR, Schwab RJ, Dinges DF. A survey
screen for prediction of apnea. Sleep. 1995;18(3):158-66. Epub 1995/04/01. PubMed PMID: 7610311.
70.
Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to
patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46(10):1121-3.
Epub 1989/10/01. PubMed PMID: 2803071.
71.
Ottoni GL, Antoniolli E, Lara DR. The Circadian Energy Scale (CIRENS): Two Simple Questions for a
Reliable Chronotype Measurement Based on Energy. Chronobiology international. 2011;28(3):229-37.
72.
Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2014
Codebook Report. Atlanta, GA: CDC; 2015.
73.
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav.
1983;24(4):385-96. Epub 1983/12/01. PubMed PMID: 6668417.
74.
Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety
disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-7. Epub 2006/05/24. PubMed PMID: 16717171.
75.
Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social
support. J Personality Assessment. 1988;52(1):30-41.
76.
Centers for Disease Control and Prevention. Measuring healthy days. Atlanta, GA: CDC; 2000.
77.
Radloff LS. The CES-D scale: A self-report depression scale for research in the general population.
Applied Psychological Measurement. 1977;1:385-401.
78.
Montgomery-Downs HE, Insana SP, Bond JA. Movement toward a novel activity monitoring
device. Sleep Breath. 2012;16(3):913-7. Epub 2011/10/06. PubMed PMID: 21971963.
79.
FitBit Inc. FitBit FLEX Product Manual. San Francisco: FitBit; 2013.
80.
Hughes S, Hankins MW, Foster RG, Peirson SN. Melanopsin phototransduction: slowly emerging
from the dark. Prog Brain Res. 2012;199:19-40. PubMed PMID: 22877657.
81.
Pail G, Huf W, Pjrek E, Winkler D, Willeit M, Praschak-Rieder N, Kasper S. Bright-light therapy in
the treatment of mood disorders. Neuropsychobiology. 2011;64(3):152-62. Epub 2011/08/04. PubMed
PMID: 21811085.
82.
Bailes HJ, Lucas RJ. Melanopsin and inner retinal photoreception. Cell Mol Life Sci. 2010;67(1):99111. Epub 2009/10/30. PubMed PMID: 19865798.
83.
Chang AM, Aeschbach D, Duffy JF, Czeisler CA. Evening use of light-emitting eReaders negatively
affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci U S A. 2015;112(4):1232-7.
PubMed PMID: 25535358; PMCID: PMC4313820.
Table 1. Characteristics of the baseline sample
Variable
DEMOGRAPHICS
Age
Sex
Year in School
Financial Status
Living Situation
GPA
SLEEP
PSQI Bedtime
PSQI sleep latency
PSQI Wake Time
PSQI Total Sleep Time
PSQI Time in Bed
PSQI Sleep Efficiency
Category
Values
Years
Female
Male
First
Second
Third
Fourth
Fifth and above
Part time
Very Poor
Poor
Lower Middle
Middle
Upper Middle
Wealthy
Very Wealthy
On-campus residence hall
Fraternity or sorority
Other university housing
Parent’s home
Off-campus housing
A+
A
AB+
B
BC+
C
C-
19.3 ± 5.0
53.97%
46.03%
5.29%
33.33%
31.22%
23.28%
5.29%
1.59%
2.12%
7.94%
15.87%
44.97%
21.69%
6.88%
0.53%
6.88%
0.53%
11.64%
4.23%
76.72%
2.65%
16.93%
10.05%
19.58%
25.40%
9.52%
9.52%
5.29%
1.06%
Time
Minutes
% ≥30 mins
Time
Hours
% <7 hours
% 7-8 hours
% over 8 hours
Hours
Percent
% below 85%
11:15 PM ± 76.32 mins
21.5 ± 18.0
29.4%
6:57 AM ± 86.40 mins
6.97 ± 1.17
43%
45%
12%
7.60 ± 1.42
93.5% ± 16.7%
28%
PSQI Score
PSQI “Poor Sleep”
MAP Loud Snoring
MAP Snorting/Gasping
MAP Pauses in Breathing
MAP Probability
ISI Score
CIRENS (Morning Energy)
CIRENS (Evening Energy)
Never
Rarely (less than once a week)
Sometimes (1-2 times per week)
Frequently (3-4 times per week)
Always (5-7 times per week)
Never
Rarely (less than once a week)
Sometimes (1-2 times per week)
Frequently (3-4 times per week)
Always (5-7 times per week)
Never
Rarely (less than once a week)
Sometimes (1-2 times per week)
Frequently (3-4 times per week)
Always (5-7 times per week)
%
% over 30%
% mild (8-14)
% moderate-severe (≥15)
Very Low
Low
Moderate
High
Very High
Very Low
Low
Moderate
High
Very High
CIRENS Total Energy Score
FSS Score
Drowsy Driving
MENTAL WELL-BEING
PSS Score
GAD7 Score
MSPSS
CESD Score
% high (>36)
% Yes in the past month
Family Score
Friends Score
Significant Other Score
Team Score
Total Score
6.38 ± 3.28
68.25%
70.37%
12.70%
7.41%
3.17%
6.35%
82.54%
8.47%
4.76%
3.17%
1.06%
93.65%
3.70%
1.59%
0.00%
1.06%
0.114 ± 0.135
11.6%
7.70 ± 5.16
32.3%
12.2%
6.35%
23.28%
46.03%
18.52%
5.82%
4.76%
15.34%
45.50%
30.69%
3.70%
6.25 ± 1.75
29.5 ± 11.0
22.7%
17.46
23.4 ± 7.1
5.16 ± 4.46
23.4 ± 5.3
22.2 ± 5.3
22.2 ± 5.9
15.5 ± 4.8
83.4 ± 17.2
10.8 ± 7.5
HEALTH
Overall Health
Poor Physical Health
Poor Mental Health
Health Interferes
Unhealthy Days
Healthy Days
Body Mass Index
STUDENT LIFE
Practice and Competition Time
Work Time
Studying / Homework Time
Class Time
Commute / Travel Time
Strength / Conditioning Time
Practice Interferes With Sleep
Competition Interferes with Sleep
Training Interferes with Sleep
Class Interferes with Sleep
Homework Interferes with Sleep
Work Interferes with Sleep
Excellent
Very Good
Good
Fair
Poor
Days/ Month
Days / Month
Days / Month
Days / Month
Days / Month
kg/m2
26.46%
51.85%
17.46%
3.70%
0.53%
5.40 ± 9.38
5.83 ± 9.81
3.15 ± 8.02
10.3 ± 15.6
19.7 ± 15.6
24.6 ± 5.0
Hours / Week
Hours / Week
Hours / Week
Hours / Week
Hours / Week
Hours / Week
Not at all
A little
Somewhat
A lot
Very Much
Not at all
A little
Somewhat
A lot
Very Much
Not at all
A little
Somewhat
A lot
Very Much
Not at all
A little
Somewhat
A lot
Very Much
Not at all
A little
Somewhat
A lot
Very Much
Not at all
A little
16.2 ± 9.0
1.82 ± 5.49
8.85 ± 6.83
8.36 ± 6.13
3.30 ± 3.33
6.93 ± 6.37
31.75%
28.57%
24.34%
11.11%
4.23%
33.86%
33.33%
22.22%
9.52%
1.06%
26.98%
29.63%
28.04%
12.70%
2.65%
32.28%
34.39%
21.69%
10.58%
1.06%
16.40%
32.80%
25.93%
22.75%
2.12%
83.60%
8.99%
Practice Interferes with Mental
Well-Being
Competition Interferes with Mental
Well-Being
Training Interferes with Mental
Well-Being
Class Interferes with Mental WellBeing
Homework Interferes with Mental
Well-Being
Work Interferes with Mental WellBeing
Sleep Difficulties Impact
Caffeine Consumption
Somewhat
A lot
Very Much
5.82%
1.59%
0.00%
Not at all
A little
Somewhat
A lot
Very Much
47.09%
29.10%
15.87%
5.29%
2.65%
Not at all
A little
Somewhat
A lot
Very Much
46.03%
29.63%
13.23%
8.47%
2.65%
Not at all
A little
Somewhat
A lot
Very Much
50.79%
26.98%
14.81%
5.82%
1.59%
Not at all
A little
Somewhat
A lot
Very Much
41.80%
30.69%
17.99%
7.94%
1.59%
Not at all
A little
Somewhat
A lot
Very Much
38.62%
27.51%
22.75%
8.99%
2.12%
Not at all
A little
Somewhat
A lot
Very Much
This did not happen to me
Academics not affected
Received a lower project/exam
grade
Received a lower course grade
Significant disruption in work
Never
90.48%
6.35%
1.59%
1.06%
0.53%
47.09%
35.45%
15.87%
6.88%
3.17%
34.39%
Smoking
Alcohol
Marijuana
Once a month or less
Once a week or less
A few times a week
Every day
Multiple times a day
Never
Former
Occasionally
Daily
Never
Once a month or less
Once a week or less
A few times a week
Every day
Multiple times a day
Never
Rarely
Often
17.99%
13.76%
19.05%
10.58%
4.23%
92.59%
3.17%
3.17%
1.06%
35.98%
30.16%
26.46%
6.35%
0.53%
0.53%
93.12%
3.70%
3.17%
Table 2. Mean and Standard Deviation sleep diary and tracker values by week
SLEEP DIARY
# Participants
Time into bed
Sleep latency (min)
Number of awakenings
Wake after sleep onset
Time awake
Time in bed after awakening
Total time in bed
Total sleep time
Sleep efficiency
Nap minutes
SLEEP TRACKER
# Participants
Sleep Time (mins)
Time in Bed (mins)
Sleep Efficiency (mins)
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
38
23:05
(46 min)
13.7
(7.1)
1.73
(1.09)
10.5
(13.1)
6:04
(93 min)
39.2
(57.8)
475
(92)
395
(98)
0.813
(0.186)
7.36
(3.43)
38
23:08
(48 min)
12.2
(6.2)
1.57
(1.34)
8.9
(9.71)
6:37
(84 min)
20.5
(29.9)
488
(98)
428
(88)
0.857
(0.156)
5.56
(9.53)
35
23:19
(51min)
11.8
(5.9)
1.39
(1.09)
8.13
(8.96)
7:04
(38 min)
17.5
(20.5)
496
(59)
445
(54)
0.900
(0.049)
5.91
(8.47)
32
23:21
(63 min)
12.2
(8.3)
1.44
(1.45)
8.1
(1.26)
6:59
(40 min)
9.54
(8.21)
479
(69)
437
(65)
0.912
(0.053)
5.67
(6.45)
29
23:32
(51 min)
12.2
(10.7)
1.36
(1.1)
6.27
(7.66)
6:54
(46 min)
10.9
(19.4)
470
(66)
425
(58)
0.907
(0.06)
4.80
(8.89)
28
23:29
(64 min)
11.7
(10.5)
1.26
(1.07)
7.24
(0.24)
7:03
(50 min)
11.6
(12)
478
(70)
435
(68)
0.910
(0.057)
4.29
(6.15)
24
23:25
(60 min)
10.5
(6.2)
1.24
(1.3)
6.84
(9.13)
7:03
(50 min)
9.89
(1.64)
478
(45)
440
(44)
0.912
(0.068)
3.40
(6.42)
22
23:18
(63 min)
11.7
(7.5)
1.31
(1.35)
8.42
(1.78)
7:07
(67 min)
7.12
(7.95)
485
(84)
454
(69)
0.931
(0.041)
4.42
(5.83)
18
23:13
(63 min)
18.3
(15)
1.69
(1.58)
7.89
(9.64)
7:32
(64 min)
9.72
(8.8)
517
(60)
472
(50)
0.915
(0.049)
3.48
(7.21)
18
23:29 (74
min)
17.3
(16.2)
1.47
(1.31)
6.57
(8.74)
6:46
(57 min)
5.6
(6.81)
457
(85)
413
(100)
0.894
(0.128)
7.63
(2.49)
36
445.93
(76.58)
506.15
(119.3)
89.23%
(9.06%)
38
449.56
(76.80)
505.42
(108.95)
89.86%
(7.84%)
35
444.25
(77.21)
499.91
(80.63)
89.14%
(8.77%)
32
440.36
(68.75)
507.66
(117.22)
88.03%
(9.25%)
33
451.84
(77.79)
489.85
(124.83)
89.97%
(7.57%)
32
431.83
(54.93)
481.91
(57.96)
89.86%
(7.43%)
28
432.41
(65.50)
486.13
(63.79)
86.01%
(18.68%)
24
443.62
(60.04)
493.18
(64.47)
90.28%
(8.02%)
21
457.14
(80.67)
508.28
(77.41)
90.08%
(9.49%)
19
444.83
(63.53)
491.02
(56.02)
90.75%
(8.25%)
Table 3. Perceived benefits of the program
Item
What aspects of the program were helpful?
Survey
Sleep science information session
Sleep tips from information session
Daily text messages
Access to support
Sleep diary
Sleep tracker
Weekly lottery
Blue-Blocker Glasses (subgroup only)
Light Bulb (subgroup only)
What have you learned?
How sleep is important for health
How sleep is important for daytime functioning
How sleep is important for athletic performance
How sleep is important for mental well-being
How to tell if my sleep is good or bad
How I actually sleep and how this varies over time
How a good or bad night affects me the next day
How has your sleep changed?
My sleep is better
I am more satisfied with my sleep
I fall asleep easier
Awakenings at night are less of a problem
My sleep timing is better
I know what to do if I have trouble sleeping
I know what to do if I am sleepy during the day
I am more energized during the day
What other improvements have you seen?
Stress
Academic Performance
Athletic Performance
Social Life
Family Life
Mental Health
Physical Health
Energy Level
Ability to Focus
Value
Helpful (% > 5)
Very Helpful (% > 7)
74.3%
28.6%
94.3%
71.4%
91.4%
62.9%
48.6%
17.1%
62.9%
40%
65.7%
17.1%
91.6%
57.1%
88.6%
65.7%
92.9%
50%
71.4%
50%
Learned a lot (% A lot or Very much)
74.3%
85.3%
85.7%
85.7%
60.0%
80.0%
77.1%
% Agree
82.9%
82.9%
77.1%
76.3%
91.6%
85.7%
97.1%
74.3%
% Agree
65.7%
77.1%
88.6%
77.1%
71.4%
77.1%
85.7%
91.4%
82.8%