This study was a randomized, controlled trial that investigated whether restricting phone use before bedtime could improve sleep outcomes in college students with poor sleep quality.The researchers assigned 38 participants to two groups: one intervention group, where they were instructed to avoid using their phones 30 minutes before bed, and one control group, no restrictions, for four weeks. The intervention group used tactics like screen time management features on their phones or turned off their phones completely, also with researchers sending reminders before the 30 minute period. The study’s results showed significant improvements in the intervention group, as their sleep duration increased by ~18 minutes, sleep latency decreased by ~12 minutes, overall sleep quality improved, and pre-sleep energy and arousal decreased. Also, over the study, these participants reported better moods and showed better working memory performance on tested cognitive tasks. This study is useful for our project because it demonstrates that even a simple intervention, like restricting phone time before bed, can produce significant outcomes and improvements in four weeks.
Electronic Screen Use and Sleep Duration and Timing in Adults (Reid)
This article examines the association between screen use before sleep and poorer sleep outcomes in Adults. It was a large, cross-sectional study among 122,058 average age adults from the American Cancer Society’s Cancer Prevention Study. The researchers can participants self-report their own screen use (excluding TV screens) in the hour leading up to bed, as well as their wake up times and overall sleep quality. The results showed that 41% of adults reported daily screen use before bed, while only 17% reported no screen use at all before bed. When compared to the no screen use adults, daily screen use resulted in significantly poorer sleep outcomes: 33% higher frequency of poor sleep quality, ~7 minutes fewer of sleep on workdays, ~5 minutes fewer of sleep on non-workdays (totaling ~45 minutes fewer per week), and delayed bedtimes by ~19 minutes. The results suggested that screen use-related sleep disruptions come from both blue light exposure (delaying melatonin release) and cognitive arousal from consuming stimulating content. This research is important for our project because it demonstrates that screen use before bed results in significantly poorer sleep outcomes for the broader adult population, not just teens and college students, and that “night-owl” types of people are especially susceptible.
Sleep deprivation related to poor food choices for teens, study says (Clara)
This article discusses how sleep deprivation in teens is linked to poorer food choices, especially when teens have a higher sugar and carbohydrate intake. For their methodology, researchers conducted a three-week controlled sleep study manipulating adolescents’ bedtimes to compare the effects of 6.5 versus 9.5 hours of sleep per night. The subjects were 93 adolescents ages 14–17, mostly from upper middle class backgrounds. The study found that sleep-deprived teens consumed significantly more late-night calories—especially carbs and added sugars—and fewer fruits and vegetables; overall, less sleep led to “more junk” intake and higher risk factors for metabolic issues. The authors noted limitations, such as the short three-week timeframe and narrow socioeconomic sampling, and suggested that long-term patterns remain uncertain. This can be helpful for our study in understanding the ramifications of poor sleep and how we can inform our consumers about these facts – possibly using this information in our ‘reward’ to illustrate how better sleep could also have a positive effect on other areas of their lives, such as their weight.
Something Has to Give: An Urban Community-Engaged Focus Group Study of Teen Sleep (Clara)
The article explores teens’ struggle with sleep and identifies the multilevel factors—social, environmental, academic, and behavioral—that contribute to chronic exhaustion. The researchers used a community-engaged qualitative design, conducting seven focus groups and analyzing transcripts through content analysis. Participants included 46 individuals: teens, parents, teachers, school nurses, and a nurse practitioner living or working in an urban Northeastern community. Results showed that packed schedules, heavy homework, family responsibilities, phone use, mental health stressors, and early school start times all reduced nighttime sleep, leading to mood issues, decreased school performance, and pervasive exhaustion. The paper calls for future research on tailored sleep interventions, improved sleep education, and better support systems for families and schools. This strongly relates to our behavior-change study because it highlights exactly which barriers prevent teens from sleeping earlier—and shows that interventions targeting routines, phone use, and education can meaningfully support healthier sleep schedules. As a group, we will be able to use this study as a baseline towards meaningful solutions that can change our participants’ behaviours.
This study examines the effectiveness of a brief online sleep education program, called “Sleep 101,” on improving sleep behaviors and hygiene practices among college students. Researchers conducted surveys before and after completion of the online program. Participants completed a baseline survey before starting the program (which was available for seven weeks). Two weeks after the program closed, the post-study survey became available. 338 students at a mid-sized public university in the U.S. participated in the study and completed the baseline survey, and 25 completed the post-study survey. Sleep 101 covered basic sleep physiology, the impact of sleep on mood and academic performance, interactions between sleep and substances, and common sleep disorders. Results of the post-study survey showed that participants experienced an increase in weekday sleep duration (from 8.00 to 8.75 hours median) and were less likely to engage in activities detrimental to sleep before bedtime, such as using electronic devices. Additionally, 40% of completers reported improvements in their sleep quality, and 41.7% indicated pulling fewer all-nighters after the program. The study demonstrated that even a brief educational intervention can positively impact specific sleep behaviors. This article is relevant for our study because it demonstrates that scalable, low-intensity interventions can produce measurable improvements in sleep duration and pre-bedtime habits among college students.
This study examines how university students plan their bedtimes and the relationship between planning, procrastination, and actual sleep behaviors. Researchers monitored 119 students for 2-4 weeks using ecological momentary assessment (EMA) and Oura ring sleep trackers. Students reported daily whether they planned a specific bedtime and their adherence to it. Results showed students rarely planned bedtimes—only 0.93 nights per week on average (23.80% of nights). When plans existed, they were frequently overrun by an average of 46 minutes, with study/work (26.63%) and electronic leisure (26.04%) being the main reasons. Higher bedtime procrastination scores correlated with longer delays when plans were overrun. Crucially, nights with a bedtime plan resulted in going to bed ~12 minutes earlier and sleeping ~12 minutes longer compared to unplanned nights. This article is relevant for our study because it shows that simply planning a bedtime, even when imperfectly executed, improves sleep outcomes. However, it highlights a key barrier: students rarely plan at all. Our intervention should encourage the habit of making bedtime plans while addressing common obstacles like study demands and device use.
Summary: This trial sought to address suicide ideation by university students through an intervention of Cognitive Behavorial Therapy for Insomnia (CBT-I). Specifically, the article argues that insomnia is a risk factor of suicide ideation and other mental health conditions through the impaired cognition that results from insomnia. The trial tested the efficacy of Sleep Scholar, a brief, self-guided, internet-based CBT-I intervention compared to a neutral control called Building Better Habits on a sample size of 61 college students. Sleep Scholar was hypothesized to be better because other forms of CBT-I are neither self-guided nor internet-based, and of those that are, they are not brief.
| Differences between the two intervention methods (Sleep Scholar vs. Building Better Habits) | ||
| Content | Sleep restriction, cognitive restructuring of sleep beliefs, automated sleep diaries, personalized sleep recommendations, college-specific guidance | General health and wellness behaviors, routines, self-care |
| Format | Brief, self-guided, internet-based | Self-guided and internet-based |
The trial found that Sleep Scholar was well accepted and showed results of mental health and habit improvements, but at around the same efficacy as Building Better Habits. Hence, it did not demonstrate superiority over the other. For Team Bull’s purposes, this trial strongly supports the importance of addressing sleep habits (as it can even lead to suicide ideation) and that tailored habit encouragement improves a person’s mental health. On the other hand, it also suggests that over customization and duration of an intervention does not significantly improve the long term effects of an intervention. This will be important as we design our intervention study.
Summary: This trial tested whether setting sleep specific intentions and watching a short sleep hygiene video improved sleep in undergraduate students. Specifically, the trial sought to improve sleep hygiene, sleep duration, and earlier bedtimes. The method for these goals was to have the students choose 3 sleep habits they don’t already do, and intend to do them by setting an ‘if-then’ goal (e.g If I’m tempted to scroll, I will read a chapter from a book instead). The results showed that while there were signs of better sleep hygiene (e.g less phone use, less caffeine), there was no significant improvement in sleep duration or bedtimes. The study also pointed to a few possibilities as to the lack of improvement:
- Environment Factors: academic workload, unpredictable schedules, social commitments.
- Non-modifying Intervention: Students only set their intention once and did not modify it per week.
For our purpose, this study verifies that while ‘if-then’ goals are helpful, they are not a solution for long-term improvement. Additionally, it supports that a solution targeting earlier bedtimes or longer sleep duration are extremely difficult and unlikely to be successful. Instead, it might be better to focus on changing the environment of how people sleep such as phone use, caffeine, or bedroom environment.
