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Abstract Study objectivesThis study aimed to compare YASA’s automated sleep staging to manual staging in the context of a multi-night experimental sleep restriction protocol. MethodsSeventy-five adults (58% female; 57% nonwhite) participated in up to seven nights of laboratory-based polysomnography measurements. The study involved one adaptation night, followed by three nights of normal sleep (9 h time in bed) and three nights of sleep restriction (5.5 h time in bed). Condition order was counterbalanced. Manual sleep staging was performed by a registered polysomnographic technician. Sleep data were exported and processed using Python 3.12, MNE 1.8.0, and YASA 0.6.5 for automated sleep staging. ResultsAcross 483 valid sleep nights, there was 82.9% overall agreement between YASA scoring and manual scoring. Stage-specific agreement was 40.3% (N1), 85.1% (N2), 86.9% (N3), and 78.7% (REM). Agreement was higher during normal sleep nights than sleep restriction nights, particularly for wake, N1, and REM sleep classifications. ConclusionsYASA-based staging exhibited good overall agreement with manual scoring during normal sleep nights. However, caution is needed when interpreting N1 estimates, as well as in interpreting data during short sleep nights. Brief summary Current knowledge/study rationaleSleep stage scoring is typically conducted manually by at least one experienced technician, but this process is laborious and subject to biases. This study investigated whether a machine-learning-based open tool—YASA—could automatically stage polysomnography data with acceptable accuracy. Study impactAcross 430,813 epochs, YASA showed acceptable accuracy in sleep staging, making it an efficient tool for the sleep community. However, caution is still needed for some applications, such as interpreting YASA’s N1 estimates as well as data from short sleep nights. 

Abstract Study ObjectivesWe investigated the fidelity of conducting total sleep deprivation procedures outside of a controlled laboratory environment and the effects on sleepiness, stress/arousal, and learning outcomes. Materials and MethodsYoung adult participants wore sleep trackers for five days (n = 77; Mage = 19.78, 56.3% female, 55.6% white). After a baseline night, they were randomly assigned to control sleep versus total sleep deprivation conditions in their home setting. Participants in the total sleep deprivation condition were continuously monitored via web cameras and provided hourly reports on subjective sleepiness, lux exposure, and activities for one night. The next morning, participants completed stress/arousal assessments as well as a virtual lecture and immediate test on science concept learning. Following a 48-hour interval that included recovery sleep, participants repeated the subjective sleepiness, stress/arousal, and science concept test assessments. ResultsThere were low rates of withdrawal (4%) or dismissal (6%) due to non-adherence. Participants completed all hourly reports and sleep tracker data indicated high adherence to the assigned sleep condition (control sleep: M = 7.54 hours; sleep deprivation: M = 0.04 hours). Sleepiness significantly and progressively worsened following total sleep deprivation, and returned to baseline levels following recovery sleep. Sleep deprivation dampened perceived physiological arousal and impaired science concept learning; these outcomes occurred in the absence of significant changes to reported stress. Following recovery sleep, science concept test performance was similar across conditions. ConclusionsIn healthy young adults, total sleep deprivation procedures can be implemented with high-fidelity in home settings, conferring opportunities for greater ecological validity, larger sample sizes, and potentially more broadly generalizable outcomes.Statement of Significance Total sleep deprivation experiments have required continuous monitoring in a laboratory environment, which is expensive, burdensome, and low in ecological validity. The current work utilized web cameras, digital software, and mobile sleep tracking to demonstrate that sleep deprivation experiments can be conducted in participants’ home settings with high fidelity, adherence, and retention. Sleep deprivation caused worsening of sleepiness and educational learning, but recovery sleep returned these outcomes to baseline values. Though at-home sleep deprivation will not be appropriate for all research questions and study populations, this approach offers opportunities for efficient data collection of psychosocial, cognitive, and behavioral outcomes, larger sample sizes, and greater ecological validity. Furthermore, the remotely-monitored sleep deprivation approach may encourage new international collaborations for research groups across chrono-inverted time zones; this approach would allow daytime staff to monitor participants in their collaborator’s (night) time zone, thereby eliminating the need to work overnight shifts and broadening opportunities to investigate cross-cultural differences. 

Abstract Study ObjectivesMuseums are informal learning environments that attract people of all ages, but their potential for providing sleep education is underexplored. We developed interactive pop-up exhibits on sleep and investigated whether they effectively engaged museum visitors, improved sleep-related attitudes, and broadened perceptions of scientists. MethodsActivities were prototyped from 2016 to 2022, and then systematically evaluated across 11 events at a medium-sized museum. Pop-up exhibits included face-to-face interactions with sleep-research trainees, professional signage, video displays, visual icebreakers (inflatable dinosaur fitted to a CPAP mask), handouts (e.g. multilingual sleep tips, stickers, brain-shaped stress balls), and interactive activities to learn about polysomnography (magnetic “electrodes,” 3D-printed brain). Museum staff conducted qualitative evaluations and recorded the frequency and duration of visitor interactions for sleep exhibits and comparison exhibits. A subset of adult visitors completed surveys to inform acceptability, efficacy, and perceptions of scientists. ResultsA total of 1336 people visited the sleep exhibits (32% of total museum visitors), which significantly exceeded size- and location-matched comparison exhibits (12%). Visitors interacted for twice as long with the sleep exhibits relative to comparison exhibits. Survey respondents indicated that they would recommend the exhibit to a friend, that their prioritization of sleep increased, and that they intended to change their sleep habits. More than half of visitors reported scientists as being friendlier and/or more demographically diverse than they previously believed. ConclusionsPop-up museum exhibits show promise as a method to improve sleep prioritization and may have the potential to challenge stereotypes of scientists in local communities. 

Many believe that science, technology, engineering, and mathematics (STEM) students must sacrifice their sleep to achieve academic success. In striking contrast, the current research demonstrates that better sleep health in freshmen STEM majors predicted better academic success, even after accounting for mental health, fluid intelligence, and prior academic performance. The current work was the first to document that sleep health predicted better persistence in a STEM track across 2 yr. 

Female and under-represented minority students (URMs) in higher education disproportionately experience sleep disturbances. Incorporating sleep education and other psychological interventions (e.g., cognitive behavioral therapy for insomnia; CBT-I) into college settings might provide a scalable solution, but it is unknown whether such psychoeducational interventions produce equivalent outcomes across groups. We addressed this question with two approaches. First, we identified 36 US-based studies that had student participants and a psychoeducational sleep intervention. Fifteen authors shared data on sleep knowledge, quality, and/or duration separated by gender and URM groups (N = 599). Hedges’ g was computed for each group within each study. Second, we systematically reviewed the content of 21 available programs. We found that, relative to national collegiate data (59.4 % female, 41.5 % URM), females were over-represented (63.4 %) and URM groups were under-represented (23.7 %) in these studies. Interventions improved sleep knowledge (g = 0.69, p<.001) and sleep quality (g = 0.25, p<.001), but not sleep duration (g = 0.13, p = .18). Effect sizes were similar in males, females, URM students, and non-URM students. There were no studies that incorporated financial, social, or neighborhood-level barriers to sleep in students. Future interventions in student groups might be improved by combining individualized, CBT-I-based education while concomitantly addressing environmental/institutional barriers to sleep. 

Study Objectives: Sleep education programs are emerging in university settings, but educating students about sleep health does not always change nighttime behaviors. A potential solution for bridging this knowledge-behavior gap is to incorporate behavioral change techniques into sleep education programs. This study’s objective was to test the implementation intention technique in students completing an academically-demanding gateway course for medical career paths. Methods: Participants were undergraduate students enrolled in organic chemistry courses (n = 101, 64.4% females, 51.5% nonwhite). After completing baseline questionnaires and actigraphy monitoring, participants took the Sleep 101 educational program. They were randomly assigned to an Education-Only condition or to also form an implementation intention (combining sleep education with an implementation intention strategy [Education+II]). Education+II individuals formed specific plans to go to bed earlier for the next 5 school nights. Participants repeated questionnaires at midsemester (mean = 50.33 days) and next-semester (mean = 248.51 days) follow-ups. Academic performance records were extracted prior to the study, during the study semester, and during the following semester. Results: Following the educational program, both groups reported improvements in sleep quality, but only Education+II participants showed actigraphymeasured improvements in bedtimes and sleep duration. Some effects in the Education+II condition persisted at midsemester and next-semester follow-ups. Adjusting for prior academic performance, the Education+II condition showed worse academic outcomes initially (but not long term) than the Education-Only condition, particularly among students with evening circadian preferences. Conclusions: Incorporating behavioral change strategies into sleep education programs can improve sleep outcomes. The relationship between sleep and academic performance is complex in demanding academic programs. 

The first generation who engaged with digital technologies has reached the age where risks of dementia emerge. Has technological exposure helped or harmed cognition in digital pioneers? The digital dementia hypothesis predicts that a lifetime of technology exposure worsens cognitive abilities. An alternative hypothesis is that such exposures lead to technological reserve, wherein digital technologies promote behaviours that preserve cognition. We tested these hypotheses in a meta-analysis and systematic review of studies published in Medline, PsycInfo, CINAHL, Science Direct, Scopus, Cochrane Library, ProQuest and Web of Science. Studies were included if they were observational or cohort studies focused on general digital technology use in older adults (over age 50) and included either a cognitive or dementia diagnosis outcome. We identified 136 papers that met inclusion criteria, of which 57 were compatible with odds ratio or hazard ratio meta-analysis. These studies included 411,430 adults (baseline age M = 68.7 years; 53.5% female) from cross-sectional and longitudinal observational studies (range: 1–18 years, M = 6.2 years). Use of digital technologies was associated with reduced risk of cognitive impairment (OR = 0.42, 95% CI 0.35–0.52) and reduced time-dependent rates of cognitive decline (HR = 0.74, 95% CI 0.66–0.84). Effects remained significant when accounting for demographic, socioeconomic, health and cognitive reserve proxies. All studies were evaluated for quality on the basis of a standardized checklist; the primary outcomes replicated when limiting analyses to the highest-quality studies. Additional work is needed to test bidirectional causal interpretations, understand mechanisms that underpin technological reserve, and identify how types and timings of technology exposures influence cognitive health. 

Objectives: Many college athletes experience insufficient sleep due to athletic, academic, and social constraints. While prior studies have observed cross-sectional associations between poor sleep and performance in athletes, few studies have longitudinally assessed performance variations in relation to sleep measures. We investigated whether objectively measured sleep assessments were associated with peak power output improvements during a fall season of Division I collegiate baseball players. Methods: Peak power output was assessed weekly for 12 weeks in 24 male collegiate baseball players (age: 21 ± 1 years). Objective sleep (Oura ring) was assessed over the final 6 weeks to determine total sleep time, sleep efficiency, and other assessments of sleep quality, including the proportion of nights in which athletes maintained total sleep time ≥7 hours or sleep efficiency ≥90%. Bivariate correlations between sleep measures and peak power output were performed. Results: Greater improvements in peak power output throughout the fall season were associated with higher total sleep time (r = 0.429, p = .036), higher sleep efficiency (r = 0.411, p = .046), and fewer nighttime awakenings (r = − 0.495, p = .014). Furthermore, the rate of change in peak power output was associated with the proportion of nights in which athletes maintained total sleep time ≥7 hours (r = 0.557, p = .005) or sleep efficiency ≥90% (r = 0.509, p = .011). Conclusions: These findings support an association between adequate sleep duration, quality, and consistency in training season performance trajectories in college athletes.