Longer Time In Bed Research Articles

Sleep and circadian rhythm disruptions in behavioral variant frontotemporal dementia.

Sleep and rest-activity rhythm alterations are common in neurodegenerative diseases. However, their characterization in patients with behavioral variant frontotemporal dementia (bvFTD) has proven elusive. We investigated rest-activity rhythm alterations, sleep disturbances, and their neural correlates in bvFTD. Twenty-seven bvFTD patients and 25 healthy controls completed sleep questionnaires and underwent 7 days of actigraphy while concurrently maintaining a sleep diary. Cortical complexity and thickness were calculated from T1-weighted magnetic resonance (MR) images. Compared to controls, bvFTD patients showed longer time in bed (95% confidence interval [CI]: 79.31, 321.83) and total sleep time (95% CI: 24.38, 321.88), lower sleep efficiency (95% CI: -12.58, -95.54), and rest-activity rhythm alterations in the morning and early afternoon. Increased sleep duration was associated with reduced cortical thickness in frontal regions. Patients with bvFTD showed longer sleep duration, lower sleep quality, and rest-activity rhythm alterations. Actigraphy could serve as a cost-effective and accessible tool for ecologically monitoring changes in sleep duration in bvFTD patients. We assessed sleep and circadian rhythms in behavioral variant frontotemporal dementia (bvFTD) using actigraphy. Patients with bvFTD show increased sleep duration and reduced sleep quality. Patients with bvFTD show rest-activity alterations in the morning and early afternoon. Sleep duration is associated with reduced cortical thickness in frontal regions. These alterations may represent an early sign of neurodegeneration.

Association between prior-night sleep and next-day fatigue in older adults: a daily diary study

BackgroundFatigue is known as an element of frailty. Sleep problems (e.g., short sleep duration and low sleep quality) can increase fatigue, but the day-to-day relationship between sleep and fatigue has not been studied well in older adults. Using a daily diary method, this study examined the within- and between-person associations between sleep and fatigue in older adults.MethodsThe study recruited 56 Japanese community dwellers (age: 82–86 years; female: 37.5%). Participants responded to a daily diary questionnaire at the end of each day. Over seven days, time in bed and satisfaction were measured after waking up, whereas fatigue was assessed before going to bed. We included person-level covariates (demographic factors, and physical and mental health) and day-level covariates (time in study, and positive and negative emotions). Multilevel models were estimated to examine within- and between-person associations.ResultsAt the within-person level, on days following short and long time in bed and days following low levels of sleep satisfaction, individuals felt higher levels of fatigue compared with usual days. At the between-person level, no statistically significant differences in fatigue were observed between individuals with long and short time in bed.ConclusionsThe findings suggest that prior-day sleep is associated with next-day fatigue in older adults. Long and short sleep duration and low sleep quality can lead to fatigue. Considering that sleep is a modifiable health behavior, appropriate management of sleep behavior may reduce fatigue.

Sleep and Inflammation during COVID-19 Virtual Learning in Adolescents with Overweight or Obesity.

(1) Background: Adolescents present as a high-risk group for a range of adverse physical health outcomes during the pandemic, including sleep and C-reactive protein (CRP) levels. As adolescents with overweight or obesity (OWOB) present as an even higher risk group, the present study assessed relationships between sleep and CRP levels before and during COVID-19 in adolescents with OWOB. (2) Methods: Fourteen adolescents with OWOB participated in a pre-COVID1, pre-COVID2, and during-COVID-19 lab visit, measuring sleep and CRP levels. The sample size was limited by the number of participants who provided data before COVID-19 and who were enrolled in virtual school during the recruitment phase. However, our power analyses indicated needing a minimum of 10 participants to achieve adequate power. Pre-COVID1, pre-COVID2, and during-COVID-19 normative expected CRP levels were calculated based on age, sex, race, and body mass index percentile-matched data. Analyses compared pre-COVID1 and pre-COVID2 sleep with during-COVID-19 sleep, during-COVID-19 sleep and during-COVID-19 CRP levels, during-COVID-19 CRP levels with normative expected during-COVID-19 CRP levels, change in CRP levels from pre-COVID1 and pre-COVID2 to during-COVID-19 with normative expected CRP levels during those time periods, and change in CRP levels before COVID-19 with change in CRP levels during COVID-19. (3) Results. During COVID-19, participants experienced decreased sleep efficiency (p = 0.001), later wake time (p < 0.001), longer time in bed (p = 0.021), and onset latency (p = 0.004), compared to pre-COVID1, and decreased sleep efficiency (p = 0.002), longer onset latency (p = 0.006), and later wake time (p < 0.001) and bedtime (p = 0.016) compared with pre-COVID2. During-COVID-19 CRP levels were positively correlated with during-COVID-19 wake times (p = 0.01) and times in bed (p = 0.008). During-COVID-19 CRP levels were greater than normative expected CRP levels (p < 0.001). CRP levels increased more from pre-COVID1 and pre-COVID2 to during-COVID-19 than normative expected changes in CRP levels (p < 0.003). Changes in CRP levels before and during COVID-19 were not significantly different. (4) Conclusions. These findings highlight the consequential effects of COVID-19, including impairments in sleep, on adolescents with OWOB. CRP levels increased more (~5 mg/L) during COVID-19 than normative expected change.

Patients with cluster headache show signs of insomnia and sleep related stress: results from an actigraphy and self-assessed sleep study

BackgroundCluster headache (CH) is a primary headache disorder which is characterized by circadian timing of headache attacks, usually at nighttime, in around two thirds of patients. Patients with CH often report sleep difficulties, though it is unknown whether this is a cause or a consequence of nightly headache attacks.ObjectiveIn this case-control study we have assessed sleep quality in study participants with CH in cluster bout respectively in remission, compared to a control group of neurologically healthy individuals to investigate the potential connection between sleep and CH.MethodsFifty study participants with CH and 42 controls were recruited for sleep assessment. Sleep was recorded using MotionWatch 8 actigraphs (CamNTech) for a period of two weeks. Study participants were instructed to wear the unit during rest and sleep and to fill out a sleep diary daily through the two-weeks period.ResultsResults from actigraphy recordings and sleep diaries suggested that patients with CH spend longer time in bed than controls (CH 8.1 hours vs. Controls 7.7 hours, p=0.03), but do not sleep more than controls (CH 6.7 hours vs. controls 6.5 hours, p=0.3). In addition, CH patients reported increased sleep latency (p=0.003), particularly during, but not restricted to, cluster bouts. Study participants with CH further reported higher levels of stress at bedtime (p=0.01), and they felt less well rested than controls (p=0.001).ConclusionOur analysis suggests that sleep is negatively affected in CH both in cluster bout and in remission, manifesting in symptoms consistent with insomnia such as prolonged sleep latency and increased time in bed.

Combining data and mathematical models for the design of personalised light interventions to improve sleep timing in aging

AbstractBackgroundMore than 70% of people living with dementia (PLWD) experience sleep disturbances (e.g., early bedtime, long time in bed) even in the early stages of cognitive decline. Light interventions have been proposed as a promising non‐pharmacological approach to improve sleep timing, but current implementations are burdensome and not personalised. Quantitative tools for designing pragmatic light interventions for the home‐setting could benefit PLWD and their carers.Here, we report on a quantitative computational tool which combines readily collectable data with a new mathematical model to provide personalised advice on light interventions. Our focus is on designing low‐burden interventions requiring minimal changes to lifestyle or sleep‐wake routines.MethodEighteen older adults (65‐80 y) monitored their light exposure and sleep for a period of 7‐10 days at home using a wristworn monitor (Actiwatch Spectrum) and a sleep diary from which we calculated daily sleep timing and duration.We constructed a new mechanistic mathematical model for sleep timing that incorporates sleep homeostasis, circadian rhythmicity and light data (HCL). For each participant, we fitted the model to their sleep timing and duration data. We then used simulations to predict the effect of different light interventions on sleep timing and used optimisation to determine light exposure patterns that promote a target sleep onset time.ResultIndividuals varied in their sleep duration (07:18 ± 0:56 hh:mm; mean ± SD) and mid‐sleep timing (03:22 ± 01:00 hh:mm). We successfully retrieved ‘personal’ model parameters that accurately captured sleep duration and timing for 17 of the 18 participants. Our simulations indicated that the effect of a given light exposure pattern on sleep timing depends on the personalised model parameters, suggesting that an intervention that works for one person may not work for someone else. We were able to propose light exposure patterns requiring minimal behavioural change, facilitating adherence.ConclusionIt is possible to extract individual physiological parameters for sleep‐wake regulation from data collected in the field. These individual parameters can be used to design personalised light interventions. Our fully‐documented code could be combined with passive sleep monitors and environmental light sensors to unobtrusively recommend personalised light exposure patterns in close‐to real time.

0622 Clustering bipolar disorder risk variants by their effect on sleep and circadian traits

Abstract Introduction Bipolar disorder (BD) is a mood disorder that causes noticeable, sometimes extreme, changes in mood and behaviour. BD affects nearly 50 million individuals worldwide including 2-4% of the US population. Previous studies have reported sleep and circadian disturbances in patients with BD. Genetic studies have identified 63 risk variants for bipolar disorder (BD). Methods To enable new insights into BD disease causing pathways, we clustered BD risk variants by their effect on sleep and circadian rhythm traits. A Bayesian soft-clustering approach was used to determine if BD variants cluster according to effect on eight subjectively and two objectively measured sleep and circadian traits (using sleep and circadian trait GWAS from the UK Biobank in European ancestry, n = 453,000). Results We found evidence of genetic correlation between BD and sleep/circadian traits (p&amp;lt; 0.05). Bipolar risk variants cluster into two clusters based on their association with sleep and circadian traits. The two clusters are: Cluster 1: late chronotype, shorter time in bed (by actigraphy), difficulty waking up, longer sleep duration (self-report), longer periods of daytime sleepiness, longer and frequent naps and increasing insomnia and Cluster 2: early chronotype, longer time in bed (by actigraphy), longer sleep duration (self-report) and less insomnia. In general, cluster 1 appears to be contributing to sleep insufficiency, while cluster 2 appears to be contributing to hypersomnia or more “typical” sleep. Conclusion In conclusion, sleep and circadian trait clusters indicate bipolar genetic risk may function through two distinct mechanisms. Some genetic risk variants appear to be related to sleep insufficiency, while others are related to hypersomnia, suggesting that it may be possible to genetically subtype BD. This finding may allow classification of BD-patients by distinct genetic pathways linking to their sleep-circadian disturbances, potentially offering a way forward toward genetically informed diagnosis, surveillance and management of BD-patients. Support (if any) N/A

The influence of a permanent double-shift school start time on adolescent sleep and chronotype across different age groups

ABSTRACT The main objective of this study was to explore the differences in sleep habits and chronotype across different age groups in Mexican adolescents attending a permanent double-shift school system. This cross-sectional study consisted of 1,969 (1,084 girls) students from public elementary, secondary, and high schools, as well as undergraduate university students from Mexico. Age range was 10–22 [15.33 ± 3.28 (mean ± SD)] years, 988 morning shift and 981 afternoon shift students. Questions regarding usual self-reported bedtime and rise time were collected, and from that, estimates for time in bed, midpoint of sleep, social jetlag, and chronotype were evaluated. Afternoon shift students reported later rise times, bedtimes, midpoint of sleep, and longer time in bed on school days than morning shift students, as well as less social jetlag. Overall, afternoon shift students reported a later chronotype than morning shift students. Peak lateness of chronotype in afternoon shift students was at age 15, with girls peaking at age 14 and boys at age 15. Meanwhile, morning shift students reported peak lateness of chronotype around age 20. In this study, adolescents from different age ranges attending an extremely delayed school start time reported adequate sleep compared with adolescents attending a fixed morning school start time. In addition, the analysis presented in this study seems to suggest that the peak of late chronotype may be influenced by school start times.

Quantifying teenagers’ sleep patterns and sex differences in social jetlag using at-home sleep monitoring

Sleep plays an important role in the formative developmental processes occurring during the teenage years. At the same time, teenagers' changing bioregulatory mechanisms and psychosocial factors converge into the so-called social jetlag, a sleep timing misalignment between weekdays and weekends. The aim of this study was to quantify the course of day-to-day changes in sleep/wake patterns and sleep stage distributions, and the sex differences in social jetlag among teenagers. We observed the sleep of 156 teenagers (58.3% girls, 15–16 years) using a novel sleep monitor over the course of up to 10 consecutive days. 1323 nights of data were analyzed using multilevel modeling. On average, participants went to bed at 23:41, woke up at 07:48, slept for 7.7 h and had 85.5% sleep efficiency. Sleep stage distributions were in line with normative data. We found later sleep onset and offset, longer time in bed, sleep duration, and sleep onset latency (p = .001), greater proportion of light sleep and lower proportion of deep sleep, and poorer sleep efficiency (all p < .001) on weekend nights starting on Friday and Saturday. On Friday nights, girls had longer time awake after sleep onset (p = .020) than boys. On Friday and Saturday nights, girls fell asleep earlier (p < .001 and p = .006, respectively). On Saturday nights, girls had shorter sleep latency (p = .024), and better sleep efficiency (p = .019) than boys. In sum, teenagers’ sleep patterns reflected healthy, albeit somewhat short sleep. There was convincing evidence of social jetlag, and girls exhibited less severe social jetlag than boys.

Association Between Variations in Training Load, Sleep, and the Well-Being of Professional Hockey Players.

To evaluate whether sleep quantity and quality of professional hockey players is affected by external training load (TL), their perception of well-being, and contextual factors associated with match participation. Fifty male athletes were monitored daily during the 28weeks of the regular season using well-being and sleep surveys. On-ice external TL was monitored using portable inertial measurement units during practices and matches. Linear mixed-effects models were applied to evaluate whether well-being perception (ie,pain, nutrition, stress, and rest) and external TL may affect sleep quality and quantity. High levels of well-being positively affected sleep duration and quality (P < .001), whereas high-intensity TL had a detrimental impact on sleep duration (P = .007). Moreover, away and evening matches had a detrimental effect on sleep quantity and quality (P < .001). Finally, a high match on-ice load per minute had a negative association with sleep quality (P = .04). Findings indicate that well-being and high-intensity trainings can impair sleep duration and quality. In addition, high-intensity match load, away matches, and evening matches can impair postmatch sleep. Therefore, monitoring well-being in conjunction with TL is essential to understand sleep disturbances in athletes. Practitioners should also implement sleep hygiene strategies that facilitate longer time in bed after high-intensity, away, and evening matches to help athletes recover.

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices.

Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability. The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling. More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism). Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Poor Sleep Quality, Eveningness, and Anxiety Are Associated With Internet/Smartphone Addiction in Korean Adolescents

Objective: Internet addiction and smartphone addiction are emerging mental health concerns in adolescents. To determine the factors related to these conditions, we investigated their associations with sleep quality, chronotype, sleep patterns, daylight exposure, depression, and anxiety.Methods: Self-rated questionnaires, including the Internet Addiction Proneness Scale-Short Form, Smartphone Addiction Scale–short version, Insomnia Severity Index, Morningness–Eveningness Questionnaire, Epworth Sleepiness Scale, sleep-related questionnaires, and Hospital Anxiety and Depression Scale, were administered in a study population of 1,610 Korean high school students. Multiple linear regression analyses were performed to evaluate the associations with internet addiction and smartphone addiction.Results: Multiple linear regression showed significant associations of internet addiction with poor sleep quality (β=0.094, p&lt;0.001), eveningness (β=-0.071, p=0.006), less daylight exposure (β=-0.066, p=0.005), anxiety (β=0.252, p&lt;0.001), and older age (β=0.295, p&lt;0.001). Smartphone addiction was significantly associated with poor sleep quality (β=0.076, p=0.005), eveningness (β=-0.164, p&lt;0.001), excessive daytime sleepiness (β=0.109, p&lt;0.001), longer time in bed on weekdays (β=0.057, p=0.024), shorter weekend oversleep (β=-0.081, p=0.005), anxiety (β=0.192, p&lt;0.001), and younger age (β=-0.145, p&lt;0.001).Conclusion: Our findings suggest that poor sleep quality, eveningness, and anxiety can serve as predictors of both internet addiction and smartphone addiction in adolescents. Internet addiction seems to be negatively associated with daylight exposure, whereas smartphone addiction showed significant associations with sleep patterns. Additional studies regarding the causal relations between these factors are needed.

Depressive Symptoms Are Associated With Accelerometer-Measured Physical Activity and Time in Bed Among Working-Aged Men and Women

Background: Depression is a significant health problem, whereas higher physical activity (PA) associates with fewer depressive symptoms. We examined how self-reported depressive symptoms are associated with accelerometer-measured PA, standing, sedentary behavior, and time in bed (TIB) among 20- to 69-year-old men and women. Methods: The study is a part of the cross-sectional, population-based FinFit2017 study, in which depressive symptoms were assessed by modified nine-item Finnish version of the Patient Health Questionnaire, and physical behavior in terms of PA, sedentary behavior, standing, and TIB was assessed 24/7 by a triaxial accelerometer. During waking hours, the accelerometer was hip worn. Intensity of PA was analyzed by mean amplitude deviation and body posture by angle for posture estimation algorithms. During TIB, the device was wrist worn, and the analysis was based on the wrist movements. A total of 1,823 participants answered the nine-item Finnish version of the Patient Health Questionnaire and used the accelerometer 24 hr at least 4 days per week. Results: Men without depressive symptoms had on average more standing, light, and moderate to vigorous PA and steps, and less low and high movement TIB than the men with at least moderate symptoms, when age group, education, work status, marital status, and fitness were adjusted for. The asymptomatic women had more moderate to vigorous PA and steps and less high movement TIB than the women with at least moderate symptoms. Conclusions: Depressive symptoms were associated with lower levels of PA and longer TIB. It is important to identify these symptoms as early as possible to be able to initiate and target preventive actions, including PA promotion, to these symptomatic persons on time.

Effects of Low-Concentration Carbon Dioxide Exposure at Bedtime on Sleep in Adults With Insomnia Symptoms

Objectives: Carbon dioxide (CO2) accumulation in an enclosed space might cause drowsiness. This study aimed to assess the effect of bedtime exposure to low concentrations of CO2 gas on the sleep quality of adults with insomnia.Methods: In this double-blind, randomized, sham-controlled cross-over study, we consecutively recruited 24 adults (9 men; age, 55.4±6.3 y) complaining of sleep disturbances (Pittsburgh Sleep Quality Index ≥5). The following two interventions were used in the study: exposure to 2% low-concentration edible CO2 gas (experimental intervention) and exposure to room air (sham-controlled intervention). A sleep air device (Gosleep®, NYX), which atomizes CO2 gas, was used in both experimental and sham-controlled interventions. For the sham session, room air was generated for 18 min, and then, gradually reduced by degrees in the next 5 min with the device finally being turned off.Results: The group exposed to CO2 gas had longer time in bed (361.3±55.2 min vs. 347.2±35.7 min, p=0.034) and total sleep time (311.0±74.3 min vs. 287.2±69.4 min, p=0.010) than the group exposed to room air. Meanwhile, the total arousal (19.7±9.5/h vs. 24.0±13.7/h, p=0.011) and non-REM arousal (20.9±11.3/h vs. 25.1±14.9/h, p=0.008) indices were lower in the group exposed to CO2 gas than in the group exposed to room air. However, perceived total sleep time or sleep latency and Karolinska Sleepiness Scale scores were not significantly different between the two groups.Conclusion: Exposure to low concentrations of CO2 improved the total sleep time and arousal index in adults with insomnia. Unexpectedly, CO2 administration was found to be effective for sleep maintenance.

Comparison of activPAL and Actiwatch for Estimations of Time in Bed in Free-Living Adults

Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p &lt; .001) were observed. There was not a significant interaction between accelerometer and day of wear (p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.

0213 Circadian Entrainment and Cognition in College Students using Antidepressants

Abstract Introduction Depression, sleep disturbances, and cognitive deficits often co-occur. Compared to non-medicated controls, people using antidepressant medications (particularly serotonin agonists) often have improved sleep including increased slow wave sleep, quality and continuity. Improvements to cognition after antidepressant treatments are also common including benefits to reaction time, inhibition, and memory. Importantly, circadian deficits, including sleep onset latency, fragmentation, and phase shifts are common in patients with depression, yet the effect of antidepressant medication on circadian indicators is unclear. The current project examined sleep, circadian entrainment, and cognition in college students who were diagnosed with depression and using serotonin agonists and in those without depressive symptoms. Methods Participants completed cognitive tasks that assessed reaction time, memory, attention, inhibition, and logical reasoning via the Automated Neuropsychological Assessment Metric between 1100-1700 two times, first at study enrollment and second after a two-week study interval. After initial cognitive assessments, participants wore actigraphs for two weeks from which we extracted their sleep-wake patterns, total sleep time(TST), time in bed(TIB), sleep-onset latency(SOL), sleep efficiency, and wake after sleep onset(WASO). Study 1 included 15 undergraduate students (11F, 7 antidepressant users, Mage=19.53) and Study 2 included 25 graduate students (21F, 13 antidepressant users, Mage=25.37), all recruited from James Madison University. Results Study 1 found that undergraduate antidepressant users had longer TIB, TST, and SOL. No cognitive or entrainment differences were found between drug use conditions. Within the antidepressant group, higher dosage predicted shorter TIB(r2=.64, p&amp;lt;.05), poorer spatial processing speed(r2=.73, p&amp;lt;.05) and logical relation performance(r2=.81, p&amp;lt;.05). In study 2, no circadian or cognitive differences were present across drug conditions. Deficits in WASO and sleep efficiency for antidepressant users were present. Within the antidepressant group, years of antidepressant use and dosage predicted poorer reaction time(r2=.84, p&amp;lt;.05), inhibition(r2=.70, p&amp;lt;.05), and spatial processing(r2=.98, p&amp;lt;.05). Self-reported wake episodes also predicted poorer reaction time and inhibition. Years of antidepressant use, dosage, and time of antidepressant use predicted TIB(r2=.73, p&amp;lt;.05), and time of antidepressant use predicted TST(sr2=.79, p&amp;lt;.05). Conclusion Future studies should use longitudinal research designs and prospective methods to examine the impact of antidepressant medications on circadian rhythms, sleep and cognition. Support (If Any)

0343 In-person Expert Pillow Fitting Process Improves Sleep Quality and Duration

Abstract Introduction The myriad of anecdotal claims about the benefits of different pillow types may complicate consumers’ ability to find a pillow that can support better sleep. Services that provide consumers with personalized recommendations may address this problem. This study examined the sleep of research participants using pillows selected during an in-store pillow fitting process compared to using their original pillows. Methods Healthy adults (71% female, ages 21-59) who reported interest in a new pillow participated in a 9-week field study using a pre-post intervention design. The intervention consisted of being guided through the Mattress Firm in-store pillow fitting process to be paired with a pillow that provided the best fit and support based on body type and sleeping position preferences. During the 4-week baseline period, participants used their regular pillow. Once they were fit for their new pillow, participants slept with that pillow for 5 weeks, including a 1-week adjustment period. Sleep was measured objectively using SleepScore Max every night and by daily and pre-post self-report. Multilevel regression and paired t-tests and were used to test for statistical significance. Results There were 722 nights of tracked sleep across participants (n=17). Objective sleep measurements showed nightly improvement in SleepScore, a measure of objective sleep quality (p=.003), longer time in bed (+13 minutes, p=.034),longer sleep duration (+16 minutes, p=.010), a lower proportion of the night awake after falling asleep (p=.009), and better sleep maintenance (p=.009). In addition, participants got more deep sleep (+5.6 minutes, p=.017) and showed improved BodyScore, a measure of deep sleep (p=.031). Self-reported measures showed improvements in pillow comfort and overall comfort in bed; perceived ability to sleep through the night; perceived reduction in number of awakenings and amount of time awake at night; better overall sleep quality; and feeling more well-rested in the morning when using the fitted pillows compared to baseline (all ps &amp;lt;.05). Conclusion Objective and self-reported sleep improved among healthy adults using a pillow selected by an expert, in-person pillow fitting process. The results suggest that a pillow fitting process can improve sleep by helping individuals find a pillow that provides a personalized fit and optimal comfort. Support (If Any) Mattress Firm INC

0182 Childhood sleep is longitudinally associated with adolescent alcohol and marijuana use

Abstract Introduction Worse sleep health has been linked with greater substance use among adolescents. However, most studies have only cross-sectionally examined this relationship or examined these longitudinal effects in children with sleep disorders. This study investigates whether childhood sleep is longitudinally associated with adolescent alcohol or marijuana use. Methods We analyzed data from the Fragile Families and Child Wellbeing Study, a longitudinal birth cohort. Parents reported their child’s bedtime at ages 3, 5, and 9, and their child’s average sleep duration at ages 5 and 9. At age 15, adolescents self-reported their bedtime, time in bed, whether they ever drank alcohol without parents, and whether they ever tried marijuana. Only participants with complete data were included (N=1,493). Logistic regression analyses for each substance use outcome were adjusted for age, sex, race, family socioeconomic status, family structure, and caregiver education level. Results Later bedtime at age 3 was longitudinally associated with lower odds of ever drinking alcohol at age 15 (OR=0.73, CI=0.58, 0.91, p&amp;lt;0.01) whereas later bedtime at age 9 was associated with greater odds (OR=1.44, CI=1.10, 1.89, p&amp;lt;0.01). Later bedtime at age 15 was cross-sectionally associated with greater odds of ever drinking alcohol (OR=1.40, CI=1.23, 1.59, p&amp;lt;0.01). Later bedtime at age 5 was associated with greater odds of ever trying marijuana (OR=1.25, CI=1.00, 1.57, p&amp;lt;0.05), as was later bedtime at age 15 (OR=1.34, CI=1.19, 1.51, p&amp;lt;0.01). Additionally, longer sleep duration at age 9 was longitudinally associated with lower odds of ever trying marijuana (OR=0.84, CI=0.74, 0.97, p&amp;lt;0.02). Adolescents who had longer time in bed at age 15 had lower odds of ever drinking alcohol (OR=0.72, CI=0.63, 0.81, p&amp;lt;0.01) and ever trying marijuana (OR=0.89, CI=0.79, 0.99, p&amp;lt;0.04). Conclusion In general, earlier bedtimes and longer sleep duration during childhood and adolescence were associated with lower odds of ever using alcohol or marijuana during adolescence. These results are consistent with current literature indicating that healthy sleep is associated with reduced risk-taking behaviors. Future research should further investigate whether sleep patterns across childhood are linked to decision-making and risk-taking behaviors in adolescence. Support (If Any) R01HD073352 (to LH), R01HD36916, R01HD39135, R01HD40421

0120 Menstrual Cycle-Related Changes in Sleep-Dependent Emotional Memory Consolidation

Abstract Introduction Emotional, more than neutral, experiences are preferentially consolidated during sleep. Fluctuating reproductive hormonal levels across the menstrual cycle are associated with changes in sleep features that are implicated in emotional memory consolidation. Yet, the interaction between menstrual cycle phases, sleep, and emotional memory remains unknown. The current study investigates how sleep-dependent emotional memory consolidation changes across the menstrual cycle in both young and midlife women. Methods Thirty-one young (MAge=25.32y, SD=5.69y) and 33 midlife (MAge=47.88y, SD=2.86y) women enrolled in the study. Each woman completed four remote visits at different menstrual phases, estimated from self-reports and measured ovulation: menses (low hormone), late-follicular (high estrogen), mid-luteal (high estrogen and progesterone), and late-luteal (falling hormones). During each visit, participants completed the Emotional Picture Task (EPT) via Pavlovia, a web-based testing platform. Participants completed encoding and immediate test in the evening (Test 1) and retested (Test 2) the following morning. During encoding, participants viewed negative and neutral images (selected from the IAPS). During testing, for each image, participants reported if it was old or new, and rated the arousal (low to high) and valence (neutral to negative). We measured dPrime at each test and calculated a difference score for overnight memory improvement. Sleep was recorded using Oura ring, a multi-sensor wearable sleep tracker. Mixed linear models determined the effect of menstrual phase on EPT, with visit as a covariate, and relevant sleep features. Results Irrespective of age, menstrual cycle phase (p&amp;lt;0.05) predicted overnight dPrime change score for negative images. Specifically, memory improvement was the highest during mid-luteal, while menses showed the most forgetting. In addition, longer time in bed was associated with less forgetting (p&amp;lt;0.01). These effects were not observed for neutral images (ps&amp;gt;0.05). Conclusion Menstrual phases are characterized by specific sex hormone profiles that can interact with both memory and sleep. The current findings indicate that sleep-dependent memory may also be affected by these changes, specifically memories that have negative emotionally content are more resilient to forgetting during the midluteal phase when both estrogen and progesterone are highest. Future studies (e.g., sleep manipulation) are required to reveal the specific sleep features associated with these effects. Support (If Any) RF1AG061355 (Baker/Mednick)

Daily Sleep and Anxious and Depressive Symptoms Among Dementia Caregivers-The Mediation of Cortisol Total Output on High- and Low-Stress Days.

Dementia family caregivers report poor sleep and mood, typically attributed to chronic stress and managing frequent daily stressors. Although many studies use global retrospective reports of these indicators, the current study examines mechanisms linking sleep, negative affect, stress biomarkers, and high-/low-stress contexts at the daily level, from the biopsychosocial perspective of stressor exposure and reactivity. One hundred seventy-three caregivers (Mage = 61.97, SD = 10.66) were providing care at home and used adult day services (ADS) at least 2 days a week. Caregivers reported their bedtime, wake time, sleep quality, and nighttime problems of the person with dementia (PwD) daily for eight consecutive days, concurrently reporting noncare stressors, negative affect, and providing five salivary cortisol samples per day. We conducted multilevel analysis to examine whether caregiver sleep characteristics overnight (i.e., total time in bed, quality, and PwD sleep problems) were associated with their anxious and depressive symptoms on days where ADS were utilized versus non-ADS days (i.e., low stress vs. high stress), and whether cortisol daily total output as area under the curve (AUCg) mediated the direct associations, at the within- and between-person levels. On high-stress (i.e., non-ADS) days when caregivers had a longer time in bed than usual the night before, they were less anxious on the following day; the direct association was statistically mediated through lower cortisol AUCg during the day. Staying longer in bed than usual before an upcoming high-stress day may help dementia family caregivers better regulate cortisol stress reactivity and anxious symptoms.

Effect of sleep on weight loss and adherence to diet and physical activity recommendations during an 18-month behavioral weight loss intervention.

To examine the association between indices of sleep quantity and quality with dietary adherence, physical activity adherence, and weight loss during a behavioral weight loss intervention. Adults (n = 156) with overweight and obesity (40 ± 9 years, 84% female, BMI: 34.4 ± 4.2 kg/m2) participated in an 18-month behavioral weight loss intervention which prescribed a reduced calorie diet (1200-1800 kcal/d) and increased physical activity (300 min/wk). Body weight, indices of sleep (SenseWear armband; SWA), energy intake (EI, 3-day food records), and moderate-to-vigorous physical activity (SWA) were measured at baseline, 6, 12, and 18 months. Linear mixed effects models examined the association between sleep and weight change over time. Additional models were adjusted for covariates including age, BMI, sex, race, ethnicity, study completion, randomization, EI, and physical activity. Secondary analyses examined the association between sleep and adherence to diet and physical activity recommendations. Mean weight loss was 7.7 ± 5.4, 8.4 ± 7.9, and 7.1 ± 9.0 kg at 6, 12, and 18 months, respectively. Lower sleep efficiency, higher wake after sleep onset (WASO), more awakenings, and higher sleep onset latency (SOL) were significantly associated with attenuated weight loss (p < 0.05). Lower sleep efficiency, more awakenings, and higher SOL remained significantly associated with blunted weight loss after adjustment for covariates (p < 0.05). Later waketime, longer time in bed, longer sleep duration, higher WASO, more awakenings, and higher SOL were associated with lower odds of achieving ≥300 min/wk of moderate-to-vigorous physical activity, adjusted for covariates (FDR p < 0.05). Future studies should evaluate whether incorporating strategies to improve sleep health within a behavioral weight loss intervention leads to improved adherence to diet and physical activity recommendations and enhanced weight loss. NCT01985568.