bluuebunny/crossref_metadata_2025_split huggingface dataset:
0731 Salivary Cortisol and Sleep in Grandparents Raising Grandchildren and Non-Caregiving Grandparents
Authors: Stearns Melanie , McGovney Kevin , Curtis Ashley , Lee Soomi , Haley William , Szalacha Laura , Beversdorf David , McCrae Christina 
Published: May 2023

Abstract

Introduction
Caregivers often exhibit unhealthy/dampened cortisol profiles and sleep problems due to high stress. Grandparent caregivers (GP-CG) may be particularly vulnerable with caregiving burden and age-related changes in sleep/health. Yet there is lack of empirical evidence examining baseline cortisol and reactivity in GP-CG and how they are associated with sleep. We examined differences between GP-CG and non-GP-CG in cortisol levels pre- and post-stress tasks and the moderating role of caregiving status in the relationship between cortisol and sleep.

Methods
486 participants in the second wave of the 2009 MIDUS biomarkers project were characterized as GP-CG who cared for their grandchild for at least 1 year (n=63, Mage=61.80, 64% female) and non-GP-CG (n=423, Mage=62.98, 56.2% female). T-tests and multiple regressions (SPSS PROCESS) examined group differences and moderation by caregiving status in the associations between salivary cortisol (Salivette®; measured before and after Stroop and MATHTurner tasks) and sleep (work-day and non-work-day total sleep time-TST; frequency of Sleep Onset Latency problems-SOLfreq; 1-never to 5-always) over the past month. Analyses controlled for age, gender, cortisol collection time.

Results
GP-CG had lower baseline cortisol (M=10.35, SD=6.02) than non-GP-CG (M=12.15, SD=7.70; t(485)=2.03, p=.045). GP-CG had lower post-task cortisol reactivity (M=10.99, SD=5.83) than non-GP-CG (M=13.21, SD=7.73; t(485)=2.06, p=.040). Only non-GP-CG had a significant cortisol increase from baseline to post-task (t(1,422)=3.00, p=.001)). Caregiving status interacted with baseline cortisol (t(1, 484)=3.81, p=.04) in its association with SOLfreq (R2=.16, p=.03). Higher baseline cortisol was associated with less SOLfreq in GP-CG with no relationship in non-GP-CG. Additionally, caregiving status interacted with post-task cortisol (t(1,484)=7.79, p=.006) in its association with non-work-day TST (R2=.17, p=.04). Cortisol and TST had a positive relationship in GP-CG but a negative relationship in non-GP-CG.

Conclusion
GP-CG may have lower baseline cortisol and reactivity compared to non-GP-CG, consistent with previous literature examining individuals with prolonged stress compared to healthy controls. GP-CG’s reduced cortisol reactivity may be adaptive given that the positive relationship between cortisol and sleep in this group, contrary to the negative association found among healthy controls. Future research should utilize longitudinal data to determine the temporality of these relationships.

Support (if any)
NIA (P01AG020166, R37AG027343; McCrae, PI: R01AG061976)
0732 Sleep multi-trajectories of couples up to 36 months postpartum and their covariates: the SEPAGES cohort study
Authors: Kim Mihyeon , Lyon-caen Sarah , Pin Isabelle , Bayat Sam , Slama Rémy , Philippat Claire , Plancoulaine Sabine 
Published: May 2023

Abstract

Introduction
Parents reported degraded sleep outcomes up to several years after child birth. Most published studies were cross-sectional, in early post-partum and focusing on maternal-child dyads. We aimed at identifying sleep multi-trajectories (SMT) including night (NSD) and daytime (DSD) sleep duration and subjective sleep loss (SSL) between 3 and 36 months postpartum in couples from the SEPAGES cohort.

Methods
From 484 mothers and 410 fathers included in the SEPAGES cohort, sleep information was collected by self-reports at 3, 18, 24 and 36 months postpartum. Couple SMT were modelled using Group-Based Trajectory Modelling method. Each couple was assigned to the group of SMT with the highest probability of belonging to. Multinomial logistic regression assessed sociodemographic, parental and child covariates associated SMT

Results
Three groups SMT were identified in couple who reported sleep data at least 2 times across the 4 time-points (N=188). In G1 (29.3%), couples had 8-9hr of mean NSD and a stable and relatively short mean maternal and paternal DSD (≈10min and ≈5min, respectively) while SSL was decreasing for mother (58% to 31%) but stable for father (≈30%) between 3 and 36 months postpartum. In G2 (27.7%), they had a stable but relatively short mean NSD (7-8hr), a maternal DSD decreasing by 20min between 3 and 18 months then being stable at 23min, a stable paternal DSD (≈10min) and high but decreasing SSL from 90% to 86% for mothers and 76% to 61% for fathers. In G3 (43.0%), couples had a stable mean NSD (8-9hr), a relatively long mean DSD, decreasing from 54 to 41min for mothers but increasing from 28 to 39min for fathers, and a stable but relatively high SSL (≈70% for mothers and ≈55% for fathers). From logistic regression, belated paternal chronotype increased odds for belonging to G2 while having a first child and born in autumn decreased odds to belong to G2 compared to G1.

Conclusion
We identified 3 different SMT between 3 and 36 months postpartum for the first time. We found several factors associated with each SMT. Studies are still warranted to investigate further interrelation with the child sleep patterns.

Support (if any)
-
0737 Age, menstrual phase, and circadian preference impact sleep in menstruating individuals
Authors: Meers Jessica , Bower Joanne , Nowakowski Sara , Alfano Candice 
Published: May 2023

Abstract

Introduction
An eveningness chronotype is generally associated with reduced sleep continuity and quality, as well as social jetlag. Changes in hormones and menstrual phase similarly impact sleep, although research on the chronobiological effects of these changes are equivocal. This study aimed to examine the role of menstrual cycle in daily self-report and actigraphy-assessed sleep in two consecutive menstrual cycles.

Methods
Participants (n=51; 43% Caucasian), 18-35 years (23.67±4.68) completed actigraphy and daily sleep diaries over two menstrual cycles (m days=51.29). Four menstrual phases were identified via dates of menstrual bleeding and urinary ovulation testing: perimenstrual (±3 days of bleeding), mid-follicular, periovulatory, and mid-luteal. Relationships between menstrual phase and sleep parameters were estimated using multistep hierarchical linear modeling. Subjective and objective measures yielded the following sleep variables: Total Wake Time (TWTsub and TWTobj), Sleep Efficiency (SEsub and SEobj), and mid-sleep point (MSP). Participants completed the Morning-Eveningness Questionnaire (MEQ) at baseline.

Results
Mean cycle length was 28.61 days±2.69. Age was significantly associated with TWTobj (r=-.32, p &lt;.001); SEobj (r=.34, p &lt;.001), and MSP (r=-.42, p &lt;.001) such that older participants had shorter TWT, higher SE, and earlier MSP. TWTobj was 4-7 minutes lower during the mid-follicular (61.54±3.37) and mid-luteal phases (63.11±3.29) compared to the perimenstrual phase (67.54±3.37; p &lt;.001). SEobj was higher in the mid-luteal phase (82.50±0.79) vs. the perimenstrual phase (80.71±0.82, p =.006). Perimenstrual women reported 8-16 minutes longer awake (TWTsub=52.23±5.01, p &lt;.001) and had 1-3% reduction in SEsub (89.70±0.10, p &lt;.001). MEQ negatively predicted TWTobj (p=.004) and SEobj (p=.003), but did not vary across menstrual phase. In morning-type individuals, TST was significantly higher in the periovulatory (b =1.81, p=.01) and mid-luteal phases (b=2.03, p&lt;.001) compared to the perimenstrual phase.

Conclusion
With increasing age, women experienced reduced time awake, higher sleep efficiency, and earlier sleep timing. Eveningness predicted greater total wake time and lower sleep efficiency. With menstrual phase considered, disturbed sleep was highest in the perimenstrual phase and women reporting a stronger preference for eveningness reported shorter TST during the perimenstrual phase. These findings highlight the importance of considering the menstrual cycle in the sleep health of menstruating individuals.

Support (if any)
0740 Factor Structure of Sleep in Older Adults Differs when Nearing End-of-Life
Authors: Klingman Karen , Sullivan Suzanne 
Published: May 2023

Abstract

Introduction
Adults aged 65 and older in the last year of life are at risk for experiencing non-beneficial hospitalizations, adversely effecting their quality-of-life. Reasons for undesired hospitalizations include the need to manage symptoms of progressing chronic illnesses, many of which are exacerbated by poor sleep. Given the complexity of sleep, especially as end-of-life (EOL) nears, it is imperative to understand if the myriad of variables traditionally used to describe sleep can be simplified. Insight gained by such knowledge could be used to guide assessment of sleep at EOL and identification of sleep-related interventions to improve EOL care.

Methods
Exploratory factor analyses (EFAs) were performed using data from rounds of the National Health and Aging Trends Study (NHATS) that included in-depth sleep modules. One EFA was performed for individuals who died before the next round, and another for those who did not. Ten variables were considered for each EFA, and comprised sleep disorders symptoms, sleep quality, and daytime consequences.

Results
There were three unique constructs each for the died and not-died groups. Among NHATS participants who had died, sleep experience (&gt;30 mins falling asleep, try falling back asleep, time falling back asleep, sleep hours, and quality of sleep), tiredness (difficulty staying awake, how often nap), and EOL symptoms (sleep meds, breathing, and pain) explained 47.5% of the variability in sleep (n=122). Among NHATS participants who had not died, sleep disorder symptoms (&gt;30 mins falling asleep, try falling back asleep, time falling back asleep, sleep hours, breathing, and quality of sleep), discomfort (pain, difficulty staying awake, how often nap), and sleep medication explained 40.9% of the variability in sleep (n=124).

Conclusion
Older adults nearing the EOL have different factor structures describing their sleep than those who are not near EOL. Napping and daytime sleepiness as well as pain, medication, and breathing for sleep have important implications for those nearing EOL that could be addressed by palliative care interventions.

Support (if any)
No support was provided for this study. Dr. Klingman is funded, in part, by NINR Award Number R01 NR018979. Dr. Sullivan is funded, in part, NIA Award Number R03 AG067159.
0741 Gender Differences in the Association of Household Burden with Sleep Disturbance
Authors: Eldridge-Smith E Devon , Holm Kristen , Edinger Jack , Depew Ann 
Published: May 2023

Abstract

Introduction
The emotional, mental, and physical labor inherent to family and household management may help explain gender differences in insomnia. Research suggests that managing multiple roles and responsibilities within the household can lead to sleep disruption. However, the sleep consequences of acting as “household manager,” which has been shown to negatively impact wellbeing, remains untested. The current study evaluated the association of household burden with sleep disturbance among both women and men.

Methods
Participants were recruited to complete an online survey from both the general population and the National Jewish Health Sleep Clinic. Eligibility criteria included being in a cohabitating relationship, currently employed, and having at least one child/dependent under the age of 18 residing in the household. The sample for this analysis (n = 558; mean age = 38; 62% men) had complete data on the primary measures of sleep (PROMIS Sleep Disturbance scale) and household burden (Household Management Measure; HMM). The HMM has three subscales (Household Routines, Child Adjustment, and Household Finances), with higher scores indicating greater personal responsibility. Bivariate correlations were utilized to assess the association of household burden with sleep disturbance among both men and women.

Results
Among females, increased levels of sleep disturbance were associated with higher rates of overall household burden (r(208) = .22, p &lt; .01), as well as child (r(208) = .18, p &lt; .05), and financial (r(208) = .22, p &lt; .01) responsibility. Conversely, among males, lower levels of sleep disturbance were associated with higher rates of overall household burden (r(344) = -.20, p &lt; .01), as well as household routine (r(344) = -.14, p &lt; .01), child (r(344) = -.17, p &lt; .01), and financial (r(344) = -.20, p &lt; .01) responsibility.

Conclusion
The association between household burden and sleep disturbance differs between women and men. Among men, increased perceived household responsibility was associated with less sleep disturbance, while the opposite was found for women. Additional research is warranted to better understand the differing effects of household responsibility on sleep by gender.

Support (if any)
Funding support from the Natalie V. Zucker Research Center for Women Scholars Award.
0748 Using daily wearable cardio fitness and sleep data to predict obesity in early adolescence
Authors: Kiss Orsolya , Smith Natalia , Dooley Erin , de Zambotti Massimiliano , Baker Fiona C , Gabriel Kelley Pettee , Nagata Jason 
Published: May 2023

Abstract

Introduction
Obesity, and its co-morbidities, is a serious health risk currently facing adolescents in the US, with increased risk for adult obesity and cardiovascular health problems. Sleep is crucial for energy restorative processes and insufficient sleep influences food intake and appetite regulation. Adolescents are at risk for poor/insufficient sleep, which could, therefore, contribute to obesity risk. Given multifactorial risk factors, there is a need to identify the most salient predictors of adolescent obesity. Predicting adolescent obesity using objective Fitbit data has not previously been investigated and is the aim of the current study.

Methods
We used data collected with the Fitbit Charge 2 device from 2491 participants (Year 2, Mean-age=11.95 years, SD=0.65, 49.5% female) in the Adolescent Brain Cognitive Development (ABCD) Study®. We trained machine learning models (Gradient Boosted Trees and Regularized Logistic Regression) to identify the most important measures that predict obesity (&gt;95 BMI percentile). In addition to sleep (timing, efficiency, duration, and regularity across the week) and cardio fitness (step counts, resting heart rate) Fitbit measures, averaged across seven days, we included socio-demographic characteristics. We ran a grid search embedded in a 2x5 fold cross-validation and we evaluated the performance based on the area under the curve-AUC and accuracy-ACC metrics.

Results
15% percent of the sample were classified as having obesity. Both classification models performed acceptably high, with the LogReg having better performance (AUC-LogReg=0.76, ACC=85%) than the GBT model (AUC-GBT=0.75, ACC=0.69). Shorter sleep duration, more day-to-day variability in sleep measures, low number of steps with low variability over the week, and higher resting heart rate, with high day-to-day variability, were important predictors. Sociodemographic factors (Hispanic/Latinx ethnicity, African American race, low access to food) were also important predictors.

Conclusion
Results show that there are socio-economic disparities in obesity risk and that shorter sleep duration and more variable sleep patterns, as detected with a wearable, are important predictors of obesity risk. Results highlight the clinical utility of wearable measures and the importance of continuous monitoring of sleep, physical activity, and cardio fitness in adolescents.

Support (if any)
NIH U01DA041022
0752 Later chronotype, insufficient and disturbed sleep are linked to menstruation problems in early adolescent girls
Authors: Kiss Orsolya , Arnold Anne , Alzueta Elisabet , Harkness Anya , Kerr Erin , Arra Nicole , Durley Ingrid , Camacho Leticia , Tager Lucas , Fan Ruby , Perez-Amparan Evelyn , Nagata Jason , de Zambotti Massimiliano , Muller-Oehring Eva , Baker Fiona C 
Published: May 2023

Abstract

Introduction
Adolescence in girls is a vulnerable transitional period, characterized by substantial hormonal and physical development, the onset of menarche, dramatic brain development and changes in behavior, including sleep. Menstrual problems, including painful menses, are common among female adolescents, and contribute to school absenteeism. They may also be symptoms of gynecological conditions, which can negatively affect reproductive and general health of women. Limited work has linked sleep disturbances and menstrual problems in adolescents. Here, we examined the association between sleep behavior and menstrual problems in a large sample of adolescent girls in the Adolescent Brain Cognitive Development (ABCD) Study®.

Methods
We fitted linear mixed effect models to examine the associations between sleep behavior and menstrual problems in 1837 post-menarcheal girls (Year 3, Mean-age = 13.03, [12-15] years), assessed as part of the ongoing ABCD Study®. Sleep was assessed with the Munich Chronotype questionnaire (youth-report) and the sleep disturbance scale for children (caregiver-report). Girls answered questions about menstrual cycles and associated problems. We considered age, time since menarche, BMI, use of hormonal contraceptives, and socio-demographic characteristics in the models.

Results
Short sleep duration was related to higher menstrual pain intensity (p&lt;.01), irregular menstrual cycles (p&lt;.01) and more premenstrual symptoms (p=.01). Higher total sleep disturbance score (caregiver-reported) was also associated with higher menstrual pain intensity (p&lt;.01) and greater impact of menstrual pain on usual activities (p&lt;.01). Participants with a later chronotype (p=.04) and later wake-up time (p=.02) were more likely to experience an irregular menstrual cycle. Later wake-up time was also related to higher menstrual pain intensity and impact on daily life (p&lt;.01). Girls with recent menarche reported heavier menstrual flow (p=.02), higher menstrual pain intensity, more premenstrual symptoms, and a greater overall impact of menstrual pain on their usual activities (p&lt;.01).

Conclusion
Our results indicate multiple associations between sleep behavior and menstrual problems in adolescent girls. Both menstrual problems and insufficient sleep/sleep disturbances are important for female adolescent health and should be routinely screened for by healthcare providers and school health education providers. Future work is needed with longitudinal analyses to determine directionality of the associations we found between sleep and menstrual problems.

Support (if any)
NIH U01DA041022
0756 Amount and Timing of Sedentary Behavior and Physical Activity following Experimental Sleep Extension in Adolescents
Authors: Davidson Jordan , Simon Stacey , Cree-Green Melanie , Nadeau Kristen , Wright Kenneth , Creasy Seth , Cooper Emily , Bowen Anne , Melanson Edward 
Published: May 2023

Abstract

Introduction
Short sleep and sedentary behavior (SB) are both associated with negative health outcomes including increased risk of metabolic syndrome and obesity. Adolescence is a period characterized by decreased physical activity (PA), increased SB, and chronic short sleep. Literature about the impact of sleep duration on PA in adolescents is limited. The goal of this study was to examine changes in the amount and timing of PA and SB following a one-week sleep extension manipulation.

Methods
The sleep, PA, and SB of 34 short-sleeping (≤7h on school days) and habitually sedentary (&lt; 3h of physical activity per week) healthy-weight adolescents (age=16.4±1.1 years; 68% female; 73% non-Hispanic White) were objectively measured in a randomized cross-over study during the academic year. Participants completed one week of Typical Sleep (usual school schedule) and one week of Sleep Extension (≥1h time in bed each school night). Sleep was estimated using wrist-worn actigraphy; PA and SB were measured using a thigh-worn accelerometer. Paired t-tests compared total minutes and time blocks of PA and SB during each condition.

Results
Total SB decreased from 750.4±109.08 to 699.32±91.76 min/day during Sleep Extension compared to Typical Sleep (p= 0.018). Specifically, SB significantly decreased during Sleep Extension in the 6PM-12AM (Typical Sleep=223.1±66.6, Sleep Extension=193.8±46.2 min; p=0.04) and 12AM-6AM (Typical Sleep=22.2±36.9, Sleep Extension=5.8±9.5 min; p=0.02) time frames. There were no significant changes in light, moderate, or vigorous PA between conditions (p&gt;0.05).

Conclusion
Adolescents significantly decreased time spent in SB by nearly 1h per day during Sleep Extension with no change in PA, similar to prior research. Notably, the decrease in SB occurred during the 6PM-6AM time frame, suggesting that the additional sleep displaced time otherwise spent in SB. Reallocation of time spent in SB to PA is associated with positive health outcomes, but no research has examined reallocation to sleep time. Additional studies are needed to identify the related health effects of increased sleep duration and reduced SB in adolescents.

Support (if any)
NIH NIDDK K23DK117021; NIH BIRCWH 2K12HD057022; CTSA UL1 TR002535
0761 Differences in Sleep Barriers Between Summer and School in Adolescents with Type 1 Diabetes
Authors: Cobry Erin , Fivekiller Emily , Jaser Sarah , Karami Angela , Meltzer Lisa , Rasmussen Cristy Geno 
Published: May 2023

Abstract

Introduction
Sleep is recognized as an important component of health for people with type 1 diabetes (T1D), with significant associations identified between sleep quality, glycemic outcomes, and diabetes self-management. Adolescents, who are particularly vulnerable to insufficient sleep and have difficulty meeting glycemic targets, typically experience differences in sleep during the summer versus school months. We examined barriers to sufficient sleep during summer and fall months amongst adolescents ages 11-17 with T1D.

Methods
Adolescents (n=10, mean age 14.1 years, 40% female, 80% non-Hispanic White) with T1D participated in qualitative, semi-structured interviews to explore seasonal differences in barriers to sleep. Interviews were audio and video recorded, transcribed using Otter-AI software with manual review for accuracy, independently coded by two trained research staff, and themes were identified.

Results
Interviews were completed by five adolescents during the summer and five during school months. Barriers to achieving sufficient sleep for both time periods included diabetes-related issues (e.g. hypoglycemic episodes, glucose variability, fear of hypoglycemia, and device use), peers, activities, and family factors. School, activities, family and diabetes disruptions were equally prominent barriers during the school year, whereas diabetes disruptions were the most common barriers during the summer. While device alarms disrupted sleep at both times, adolescents reported that diabetes technologies had an overall net positive impact on sleep during the school year due to reduced worry, but a neutral impact during the summer. Adolescents agreed it would be helpful to talk about sleep concerns or ways to improve sleep.

Conclusion
Diabetes management is a consistent reason for insufficient sleep duration in adolescents with T1D. In addition, similar to adolescents without T1D, academic, social, and family obligations were barriers to obtaining sufficient sleep during the school year. While some sleep disruptors may not be modifiable, assessment by a clinician who can provide guidance on methods to improve sleep health may be beneficial and well received. Because diabetes health care providers have frequent contact, routine visits provide an opportunity to identify sleep concerns and provide both diabetes and non-diabetes guidance to address sleep health. Further exploration of sleep behaviors and barriers in adolescents with T1D is warranted.

Support (if any)
5-ECR-2022-1179-A-N, JDRF and Helmsley