BACKGROUND: Shift workers are at an increased risk of developing sleep disorders. The standard therapy recommended for sleep disorders is cognitive behavioural therapy for insomnia (CBT-I). Many of its interventions are based on a regular sleep and wake rhythm, which is difficult to apply for shift workers. We have therefore developed a new therapy manual specifically for shift workers (CBT-I-S), which should be more applicable to their needs. In particular, all interventions that require regularity have been removed, and instead, interventions that address factors that proved to be relevant to sleep in our preliminary study have been integrated. We now want to test this manual for its effectiveness.
METHODS: A randomised controlled trial with N = 142 will be conducted to compare two conditions: the newly developed therapy manual will be carried out in the experimental group, while cognitive behavioural therapy for insomnia will be employed in the standard group. Both treatments will be conducted online via MS Teams in a group setting with seven sessions each. Data will be collected at three measurement points (pre, post, 6-month follow-up) and analysed using linear mixed models. The study will investigate whether the two treatments have led to significant improvements in total sleep time, sleep onset latency, subjective sleep quality and daytime sleepiness in shift workers. It will also examine whether the new therapy manual is superior to standard therapy in shift workers and whether these effects are stable.
CONCLUSIONS: We assume that interventions designed to address depressive mood, anxiety, worry, rumination, dysfunctional thought patterns and attitudes towards sleep will also improve sleep. If this is indeed the case, these interventions could replace previous ones that require regularity. This could significantly improve the treatment of insomnia in shift workers.
BACKGROUND: German Clinical Trials Registry DRKS DRKS00032086 . Registered on August 16, 2023.

UNASSIGNED: Discrepancies between sleep diaries and sensor-based sleep parameters are widely recognized. This study examined the effect of showing sensor-based sleep parameters while completing a daily diary. The provision of sensor-based data was expected to reduce variance but not change the mean of self-reported sleep parameters, which would in turn align better with sensor-based data compared to a control diary.
UNASSIGNED: In a crossover study, 24 volunteers completed week-long periods of control diary (digital sleep diary without sensor-based data feedback) or integrated diary (diary with device feedback), washout, and then the other diary condition.
UNASSIGNED: The integrated diary reduced self-reported total sleep time (TST) by <10 minutes and reduced variance in TST. The integrated diary did not impact mean sleep onset latency (SOL) and, unexpectedly, the variance in SOL increased. The integrated diary improved both bias and limits of agreement for SOL and TST.
UNASSIGNED: Integration of wearable, sensor-based device data in a sleep diary has little impact on means, mixed evidence for less variance, and better agreement with sensor-based data than a traditional diary. How the diary impacts reporting and sensor-based sleep measurements should be explored.

Sleep is often impaired in firefighters due to the psychologically and physiologically intense nature of their work and working shift schedules. Peanut butter is affordable and a substantial source of monounsaturated fatty acids, which may aid sleep health. Thus, this study sought to determine if a daily serving of peanut butter consumed before bedtime for seven weeks altered sleep quality and quantity among full-time firefighters. Forty firefighters (peanut butter group = 20; control group = 20) participated in this eight-week randomized controlled trial. All participants completed a subjective questionnaire on mood, focus, and alertness twice daily and wore an Actigraph wristwatch to measure sleep variables, including latency, efficiency, time in bed, time asleep, wake after sleep onset, number of awakenings, and time spent awake. After a baseline week, the peanut butter group consumed two tablespoons of peanut butter two hours prior to bedtime for seven weeks. Compared to the control group, the peanut butter group did not demonstrate significant changes (p > 0.05) in sleep measures or subjective feelings of mood, focus, or alertness after consuming peanut butter for seven weeks. Therefore, peanut butter as a source of peanuts did not alter sleep quality or quantity in this group of firefighters.

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen\'s d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen\'s d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Young adults are particularly vulnerable to sleep disturbances related to their cell phone use (CPU). The purpose of this study was to test Sleep Displacement and Psychological Arousal theories of CPU-led sleep disruption in relation in a sample of university students. CPU for unstructured leisure activities before sleep (CPU_BeforeBed), as well as CPU for accessing explicit or emotionally charged media content before sleep (CPU_Arousal), were both measured. 525 (75% female) undergraduate students from a large public institution participated in this study during fall 2019. The data were collected using a battery of validated self-report questionnaires. Questionnaires included the Pittsburgh Sleep Quality Index scale, which was used to measure sleep latency and sleep difficulty, and a contemporary questionnaire developed to measure CPU_BeforeBed and CPU_Arousal. A crude odds ratio analysis was implemented to analyze findings. Sleep Displacement data showed higher odds of sleep latency occurring with CPU_BeforeBed [Exp (B) = 1.091, p < 0.001]. Psychological Arousal data showed higher odds of sleep difficulty occurring with CPU_Arousal [Exp (B) = 1.065, p < 0.001]. CPU_BeforeBed significantly predicted sleep latency [Exp (B) = 1.062, p < 0.01], and CPU_Arousal significantly predicted the sleep difficulty [Exp (B) = 1.069, p < 0.001] of undergraduate students. Young adults who used cell phones before sleep and access emotionally charged content before going to bed were more likely to report trouble sleeping. They took more time to fall asleep at night as their sleep hours were compressed due to CPU. These findings support the Sleep Displacement and Arousal theories of sleep disruption and provide further insight into possible mechanisms for sleep disturbance in young adults.

暂无摘要。

Sleep latency, the amount of time that it takes an individual to fall asleep, is a key indicator of sleep need. Sleep latency varies considerably both among and within species and is heritable, but lacks a comprehensive description of its underlying genetic network. Here we conduct a genome-wide association study of sleep latency. Using previously collected sleep and activity data on a wild-derived population of flies, we calculate sleep latency, confirming significant, heritable genetic variation for this complex trait. We identify 520 polymorphisms in 248 genes contributing to variability in sleep latency. Tests of mutations in 23 candidate genes and additional putative pan-neuronal knockdown of 9 of them implicated CG44153, Piezo, Proc-R and Rbp6 in sleep latency. Two large-effect mutations in the genes Proc-R and Piezo were further confirmed via genetic rescue. This work greatly enhances our understanding of the genetic factors that influence variation in sleep latency.

The American Academy of Sleep Medicine commissioned a task force of clinical experts in pediatric sleep medicine to review published literature on performing the Multiple Sleep Latency Test (MSLT) and Maintenance of Wakefulness Test for diagnosis and management of central disorders of hypersomnolence among children and adolescents. This paper follows a format similar to that of the paper \"Recommended protocols for the Multiple Sleep Latency Test and Maintenance of Wakefulness Test in adults: guidance from the American Academy of Sleep Medicine\" that was published in 2021. Since there is insufficient evidence to specify a recommended protocol for the Maintenance of Wakefulness Test in children and adolescents, this paper focuses only on the MSLT protocol. This protocol paper provides guidance to health care providers who order, sleep specialists who interpret, and technical staff who administer the MSLT to pediatric patients. Similar to the adult protocol paper, this document provides guidance based on pediatric expert consensus and evidence-based data when available. Topics include patient preparation, evaluation of medication and substance use, sleep needs before testing, scheduling considerations, optimal test conditions for youth, and documentation. Specific changes recommended for pediatric MSLT protocols include (1) provision of a minimum of 7 hours of sleep (with a minimum 8-hour recording time) on polysomnography the night before the MSLT, ideally meeting age-based needs; (2) use of clinical judgment to guide the need for sleep-disordered breathing treatments before polysomnography-MSLT testing; and (3) shared patient-health care provider decision-making regarding modifications in the protocol for children and adolescents with neurodevelopmental/neurological disorders, young age, and/or delayed sleep phase.
BACKGROUND: Maski KP, Amos LB, Carter JC, Koch EE, Kazmi U, Rosen CL. Recommended protocols for the Multiple Sleep Latency Test and Maintenance of Wakefulness Test in children: guidance from the American Academy of Sleep Medicine. J Clin Sleep Med. 2024;20(4):631-641.

Subjective-objective discrepancies in sleep onset latency (SOL), which is often observed among psychiatric patients, is attributed partly to the definition of sleep onset. Recently, instead of SOL, latency to persistent sleep (LPS), which is defined as the duration from turning out the light to the first consecutive minutes of non-wakefulness, has been utilized in pharmacological studies. This study aimed to determine the non-awake time in LPS that is most consistent with subjective sleep onset among patients with psychiatric disorders. We calculated the length of non-awake time in 30-s segments from lights-out to 0.5-60 min. The root mean square error was then calculated to determine the most appropriate length. The analysis of 149 patients with psychiatric disorders showed that the optimal non-awake time in LPS was 12 min. On the other hands, when comorbid with moderate or severe obstructive sleep apnea (OSA), the optimal length was 19.5 min. This study indicates that 12 min should be the best fit for the LPS non-awake time in patients with psychiatric disorders. When there is comorbidity with OSA, however, a longer duration should be considered. Measuring LPS minimizes discrepancies in SOL and provides important clinical information.

Withdrawal from cannabis use is associated with sleep disturbances, often leading to resumption of use. Less is known about the impact of abstinence on sleep in adolescence, a developmental window associated with high rates of sleep disturbance. This study investigated effects of sustained abstinence on self-reported sleep quality and disturbance in adolescents reporting frequent cannabis use.
Non-treatment seeking adolescents, recruited from school screening surveys and the community, with frequent cannabis use (MAge=17.8, SDAge=1.7, 47% female, 45% non-white) were randomized to four weeks of biochemically-verified abstinence, motivated via contingency management (CB-Abst, n=53), or monitoring without an abstinence requirement (CB-Mon, n=63). A mixed-effects model was used to predict change in Pittsburgh Sleep Quality Index (PSQI) scores.
Participants in CB-Abst reported higher overall PSQI scores than those in CB-Mon (M=1.06, p=0.01) indicating worse sleep during the four-week trial. Sleep disruptions in CB-Abst increased during Week 1 of abstinence (d=0.34, p=0.04), decreased during Week 2 (d=0.36, p=0.04), and remained constant for the rest of the trial. At Week 4, sleep was comparable to baseline levels for those in CB-Abst (p=0.87). Withdrawal-associated sleep disruption in the CB-Abst group was circumscribed to increases in sleep latency (b=0.35; p=0.05).
Cannabis abstinence in adolescents was associated with transient delayed onset of sleep initiation falling asleep during the first week of abstinence. Findings highlight withdrawal-associated changes in sleep latency as an intervention target for supporting adolescents attempting abstinence. Future research should use objective measures of sleep and focus on elucidating mechanisms underlying sleep disturbances with cannabis use and withdrawal.