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1. Sleep tight! Adolescent sleep quality across three distinct sleep ecologies

   https://doi.org/10.1093/emph/eoad040  

   Silva-Caballero, Andrea; Ball, Helen L; Kramer, Karen L; Bentley, Gillian R (January 2023, Evolution, Medicine, and Public Health)

   Abstract Background and objectivesGood sleep quality, associated with few arousals, no daytime sleepiness and self-satisfaction with one’s sleep, is pivotal for adolescent growth, maturation, cognition and overall health. This article aims to identify what ecological factors impact adolescent sleep quality across three distinct sleep ecologies representing a gradient of dense urbanity to small, rural environments with scarce artificial lighting and no Internet. MethodologyWe analyze variation of sleep efficiency, a quantitative measure of sleep quality—defined as the ratio of total time spent asleep to total time dedicated to sleep—in two agricultural indigenous populations and one post-industrial group in Mexico (Campeche = 44, Puebla = 51, Mexico City = 50, respectively). Data collection included actigraphy, sleep diaries, questionnaires, interviews and ethnographic observations. We fit linear models to examine sleep efficiency variation within and between groups. ResultsWe found that sleep efficiency varied significantly across sites, being highest in Mexico City (88%) and lowest in Campeche (75%). We found that variation in sleep efficiency was significantly associated with nightly exposure to light and social sleep practices. Conclusions and implicationsOur findings point toward contextual cost-benefits of sleep disruption in adolescence. We highlight the need to prioritize research on adolescent sleep quality across distinct developmental ecologies and its impact on health to improve adolescent wellbeing through evidence-based health practices. 

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2. YASA automated sleep staging performance across seven nights of normal sleep and sleep restriction

   https://doi.org/10.1007/s44470-026-00059-x  

   Chen, Zhiyi; Pruett, Natalya; Scullin, Michael K (December 2026, Journal of Clinical Sleep Medicine)

   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. 

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3. Reinforcing sleep education with behavioral change strategies: intervention effects on sleep timing, sleep duration, and academic performance

   https://doi.org/10.5664/jcsm.11780  

   Barley, Blake K; Scullin, Michael K (October 2025, Journal of Clinical Sleep Medicine)

   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. 

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4. Do Mosquitoes Sleep?

   https://doi.org/10.1016/j.pt.2020.08.004  

   Ajayi, Oluwaseun M.; Eilerts, Diane F.; Bailey, Samuel T.; Vinauger, Clément; Benoit, Joshua B. (November 2020, Trends in Parasitology)

   null (Ed.)
5. Sleep Patterns and Sleep Alignment in Remote Teams during COVID-19

   https://doi.org/10.1145/3555217  

   Breideband, Thomas; Martinez, Gonzalo J.; Talkad Sukumar, Poorna; Caruso, Megan; D'Mello, Sidney; Striegel, Aaron D.; Mark, Gloria (November 2022, Proceedings of the ACM on Human-Computer Interaction: Computer Supported Collaborative Work)

   Working remotely from home during the COVID-19 pandemic has resulted in significant shifts and disruptions in the personal and work lives of millions of information workers and their teams. We examined how sleep patterns---an important component of mental and physical health---relates to teamwork. We used wearable sensing and daily questionnaires to examine sleep patterns, affect, and perceptions of teamwork in 71 information workers from 22 teams over a ten-week period. Participants reported delays in sleep onset and offset as well as longer sleep duration during the pandemic. A similar shift was found in work schedules, though total work hours did not change significantly. Surprisingly, we found that more sleep was negatively related to positive affect, perceptions of teamwork, and perceptions of team productivity. However, a greater misalignment in the sleep patterns of members in a team predicted positive affect and teamwork after accounting for individual differences in sleep preferences. A follow-up analysis of exit interviews with participants revealed team-working conventions and collaborative mindsets as prominent themes that might help explain some of the ways that misalignment in sleep can affect teamwork. We discuss implications of sleep and sleep misalignment in work-from-home contexts with an eye towards leveraging sleep data to facilitate remote teamwork. 

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