bluuebunny/crossref_metadata_2025 huggingface dataset:
0201 Sleep duration and timing is prospectively linked with increases in insulin resistance over late adolescence
Authors: Jansen Erica , Burgess Helen , Chervin Ronald , O'Brien Louise , Dolinoy Dana , Tellez-Rojo Martha Maria , Cantoral Alejandra , Olascoaga-Torres Libni , Lee Joyce , Dunietz Galit , Peterson Karen
Published: May 2022

Introduction

During puberty, adolescents experience a period of transient insulin resistance (IR) that normalizes upon full maturation. Yet, IR continues to rise for some adolescents, increasing metabolic disease and type 2 diabetes risk in adulthood. Whether short sleep duration and/or later sleep timing are risk factors for persistently increasing IR in late adolescence has not been explored.

Methods

The study population includes 362 adolescents from Mexico City enrolled in a longitudinal birth cohort (ELEMENT study). Beginning in 2015, when participants were between the ages of 9 and 17, there were 2 clinic visits that occurred approximately 2 years apart. During the visit, a fasting blood sample and anthropometric measurements were taken. Insulin resistance was assessed with glucose and insulin via HOMA-IR. Four groups were defined using puberty-specific cutpoints for IR: normal HOMA-IR over the follow-up period (reference), transition from normal to IR, transition from IR to normal, and IR at both time points. Baseline sleep assessments (sleep duration, timing, and variability of both duration and timing) were measured with 7-day actigraphy. Multinomial logistic regression models were used to evaluate associations between sleep duration and timing with HOMA-IR categories, adjusting for age, sex, and baseline pubertal status

Results

Seventeen percent of the sample developed insulin resistance over the follow-up period. Adolescents ≥1 hour below the sleep duration recommendations-for-age were over twice as likely to be in the group that developed IR compared to the normal group (95% CI 1.1, .9; P for trend=0.03). Similarly, adolescents who had a sleep midpoint later than 4:36 AM were 2.77 times as likely to be in the increasing HOMA-IR category (95% CI 1.0, 7.5; P for trend=0.05). Interestingly, there was no evidence that changes in adiposity over follow-up mediated associations between sleep and insulin resistance.

Conclusion

Insufficient sleep duration and late sleep timing were independently associated with development of IR over a 2-year period in peri-puberty. Adequate sleep during the pubertal period may promote metabolic health into young adulthood, independent of any changes in adiposity.

Support (If Any)

Dr. Jansen is supported by K01HL151673. The study is supported by P01 ES02284401.
0209 The Effect of Time of Day of COVID-19 Vaccination and other Covariates on Side Effects
Authors: Abbaspour Sara , Wang Wei , Robbins Gregory , Klerman Elizabeth
Published: May 2022

Introduction

Circadian rhythms have critical roles in human health. We quantified the effect of time-of-day of COVID-19 vaccination and other covariates on self-reported side effects post vaccination.

Methods

The dataset was created from MassGeneralBrigham (MGB) electronic health records and REDCap survey that collected self-reported symptoms for 1-3 days after each immunization. Variables are demographics (age, sex, race, and ethnicity), vaccine manufacturer, clock time of vaccine administration/appointment, any COVID-19 diagnosis/positive test prior to vaccination, any history of allergy, and any note of epinephrine self-injection (e.g., EpiPen) medication. Time of day groupings were morning (6 am–10 am), midday (10 am–2 pm), late afternoon (2 pm–6 pm) or evening (6 pm–10 pm). Side effects were classified as Allergic (Rash; Hives; Swollen lips, tongue, eyes, or face; Wheezing) and Non-Allergic (New Headache, New Fatigue, Arthralgias, Myalgias, Fever) symptoms. The study was approved by the MGB IRB.Machine learning (ML) techniques (e.g., extreme gradient boosting) were applied to the variables to predict the occurrence of side effects. Stratified k-fold cross validation was used to validate the performance of the ML models. Shapley Additive Explanation values were computed to explain the contribution of each of the variables to the prediction of the occurrence of side effects.

Results

Data were from 54,844 individuals. On day 1 after the first vaccination, (i) females, people who received the Moderna vaccine, and those with any allergy history were more likely to report Allergic side effects; and (ii) females, people who received the Janssen vaccine, those who had prior COVID-19 diagnosis ,and those who received their vaccine in the morning or midday and were more likely to report Non-Allergic symptoms. Older persons had fewer side effects of any type.

Conclusion

ML techniques identified demographic and time-of-day-of-vaccination effects on side effects reported on the first day after the first dose of a COVID-19 vaccination. We will use these techniques to test for changes on days 2 and 3 after the first dose, and the first 3 days after the second dose and for the influence of recent night or shiftwork. Future work should target underlying physiological reasons.

Support (If Any)
0211 A 10-week observational research study in individuals with delayed sleep-wake phase disorder (DSWPD) symptoms
Authors: Smieszek Sandra
Published: May 2022

Introduction

We conducted an observational research study in suspected delayed sleep-wake phase disorder (DSWPD) participants. The objective was to measure sleep-wake patterns and to conduct exploratory genetic analyses to delineate the landscape of DSWPD.

Methods

We measured the sleep-wake patterns of participants by daily post-sleep diaries for 10 weeks. Participants also completed questionnaires on demographics, medical/surgical history, sleep history, and concomitant medications. Altogether, 119 participants were consented and 76 participants provided samples for whole genome sequencing.

Results

Sleep diary analysis confirmed delayed sleeping patterns in the study population. Midpoint of sleep was 4:50 AM (SD = 2:06) versus 3:06 AM (SD = 0:59) in controls, a statistically significant difference (t (df) = 6.57 (72.063); p ≤ 0.0001). Mean total sleep time (TST) was 6.88 h (SD = 1.35) versus 7.79 h (SD = 0.56) in controls, a statistically significant difference (t (df) = -5.38 (70.863); p ≤ 0.0001). This effect was driven by shorter participant-reported TST on work nights (6.33 h) versus free nights (7.22 h). Sleep latency was significantly later on work nights than free nights. Altogether 17% of participants reported at least one psychiatric condition.We observed an enrichment of the minisatellite 54bp (1: 7829913-7829966 (GRCh38)) variable number of tandem repeat (VNTR) PER3 rs57875989 4 allele. Significantly higher frequencies of the 4/4 and 4/5 variants were observed when compared to controls (n = 1937; recessive: OR 3.3, CI 2.1177 to 5.4304, p < 0.0001).We analyzed the putative loss-of-function and missense variants. We report on presence of cases with PER3 rs144178755 (NM_001289862:p.T1049), PER3 rs228696 (NM_001289861:p.L835P), and PER2 rs76355956 (NM_022817:p.V197M), among other rare nonsynonymous variants. We observed higher frequency of missense variants in core clock genes when compared to controls.

Conclusion

Sleep diary data confirmed significantly delayed sleep patterns, with more pronounced results during work nights and larger SDs across all sleep parameters, suggesting more variable sleep patterns. Genetic analysis confirmed these individuals are more likely to harbor variants within their core clock genes with enrichment of the VNTR variant, potentially leading to a pronounced delay in sleep period.

Support (If Any)

Vanda Pharmaceuticals Inc.
0215 Impact of sleep inertia on visual search performance during combined sleep restriction and circadian misalignment
Authors: Adkins Matthew , Schreiber Austin , Sprecher Kate , Burke Tina , Ritchie Hannah , Wright Kenneth
Published: May 2022

Introduction

Circadian phase and homeostatic sleep drive affect selective visual attention during sleep inertia (SI), a period of cognitive performance decrement upon awakening that dissipates with time. This study examined the combined effects of circadian misalignment and sleep restriction on visual search performance during SI.

Methods

Twenty adults (mean±standard deviation [SD] age 25.7±4.2 years; 12M, 8F) completed a 39-day protocol. Prior to a 4-day in-laboratory visit, participants maintained habitual 8h sleep schedules for 2 weeks at home. In-laboratory sleep opportunities (1 per day) were timed to induce varying levels of both circadian misalignment and sleep restriction: 8h on night 1, 3h on night 2, and 3h on mornings 3 and 4. After 3 days of recovery sleep at home (unscheduled), participants repeated the entire protocol. SI was assessed with the Serial Visual Search Task, a measure of selective visual attention, ~ 1, 16, and 31 min after electroencephalogram (EEG) verified awakening following each 3h sleep opportunity. The task required participants to determine whether a target was present or absent among nontarget distractors. Outcomes included median reaction times (RT) and number of missed targets. Sleep opportunities with ≥4.5 min of EEG-verified wakefulness in the 30 min prior to scheduled awakening were excluded from analysis. Data were analyzed with mixed-model ANOVA.

Results

Mean±SD average amount of wakefulness in the 30 min and 10 min prior to SI testing was 1.2±0.11 and 0.43±0.06 minutes, respectively. Following all sleep opportunities, median RT for overall correct responses and for correct response to target absent trials improved with time since awakening (all p<0.05). In contrast, median RT for correct responses to target present trials improved and number of missed targets worsened with time since awakening following the morning 3 sleep opportunity only (all p<0.05).

Conclusion

Reaction speed was impaired during SI regardless of circadian timing or level of homeostatic sleep drive, whereas missing targets appears to be dependent upon circadian phase and the level of homeostatic sleep debt. These findings have important implications for military and first responders.

Support (If Any)

Office of Naval Research MURI N00014-15-1-2809; NIH TR001082; Undergraduate Research Opportunities Grant, University of Colorado Boulder.
0222 A shift in the circadian timing of calories and an increase in sleep variability are associated with changes in cardiometabolic health in a real-world setting
Authors: McHill Andrew , Velasco Josie , Gillingham Melanie , Shea Steven , Olson Ryan
Published: May 2022

Introduction

Obesity and cardiovascular disease are commonplace in industrialized societies, particularly in nightshift workers. To investigate whether a shift in the circadian timing of energy intake or an increase in sleep variability may be mechanisms for adverse cardiometabolic health, we leveraged a natural experiment that occurs in newly-hired bus operators as they transition from a regularly-timed training schedule into an erratically-timed (i.e., early-morning or late evening) work schedule.

Methods

Twenty-one newly-hired bus operators (8 females, 37.1y±6.4y [mean±SD]) were studied in an ~90-day protocol. During dayshift training (i.e., baseline), participants underwent a battery of in-laboratory cardiometabolic health assessments (i.e., blood pressure [BP], blood draw, body fat percentage), and a salivary dim-light melatonin assessment (DLMO). Participants then completed daily sleep-wake diaries and recorded all food and beverages consumed using a photographic time-stamped phone application (MealLoggerTM) for one week. These measurements were repeated ~30 and ~90 days after starting their new work schedules operating a bus. Caloric intake was determined via registered dieticians and DLMO was defined using a 3pg/ml threshold. As indices of caloric timing we used the time when participants consumed 50% of daily calories (caloric midpoint), and the percent of daily calories consumed within 4h of DLMO. Sleep variability was measured as the standard deviation of sleep onset timing. Associations were calculated using Pearson’s correlations.

Results

After 90-days of working, caloric midpoint occurred 2h later than baseline (16:22±02:41 vs. 14:24±02:07; p<0.01). This shift in timing was correlated with increases in systolic BP (r2=0.28; p=0.02) and low-density lipoprotein cholesterol (r2=0.28; p=0.03). An increase in sleep variability at 90-days was also positively correlated with systolic BP (r2=0.31; p=0.02). The change in percent of calories consumed within 4h of DLMO was positively correlated with change in percentage body fat (r2=0.27; p=0.03). Last, there was no significant correlation between the change in daily calories consumed and body fat percentage (p=0.24).

Conclusion

Changes in the timing of caloric intake relative to DLMO, along with increases in sleep variability, may be mechanisms for increased cardiometabolic risk. These behaviors are potential targets for intervention to improve cardiometabolic health.

Support (If Any)

R01HL105495, K01HL146992
0224 The implication of the understudied druggable orphan GPCRs in sleep and cardiometabolic traits
Authors: Tchio Cynthia , Saxena Richa
Published: May 2022

Introduction

G-protein coupled receptors (GPCRs) are the largest class of membrane receptors. They are involved in various sleep and cardiometabolic disorders; however, 150 out of 900 GPCRs remain orphans (oGPCRs) with unknown endogenous ligands; thereby, limiting our understanding of their biological function. GPCRs are targeted by 36 % of FDA-approved drugs; thus, the understudied oGPCRs are druggable and have a high potential to impact human health once disease associations are made.

Methods

This study aimed to use a genomic approach from a large dataset to deorphanize oGPCRs whose genetic variations significantly impact sleep and cardiometabolic disorders. First, we used the UK Biobank study summary statistics to identify oGPCRs loci where multiple sleep and cardiometabolic traits colocalized at a false discovery rate < 5%. Next, in the metabolic disease knowledge portal, we performed PheWAS analysis of the variants to identify new phenotypic traits in other datasets of European ancestry. We then used GTex to identify quantitative trait loci to highlight variants that affect gene expression.

Results

Our study identified variants in oGPCRs GPR61, GPR146, and GPRC5B that have a pleiotropic effect in sleep and cardiometabolic traits in the UK Biobank cohort. The variant rs12044778 is an intronic variant in GPR61 associated with ease of waking up and morningness chronotype. GPR146's intronic variant rs1997243 showed an association with cholesterol level (HDL and LDL) and blood pressure; additionally, rs1997243 significantly increased the expression of GPR146 in adipose tissues. Finally, the intronic variant rs11639988 is associated with a decrease in BMI, and that it significantly decreased GPRC5B gene expression in the nucleus accumbens and adipose tissues.

Conclusion

Overall, our study provides new insight into the functions of oGPCRs genetic variants in sleep and cardiometabolic processes.

Support (If Any)

5T32HL007609-35
0231 Daily relationships between sleep and stress during the COVID-19 pandemic: Roles of perseverative cognition and physical activity
Authors: Baron Kelly , Shoaf Kimberley , Nicholls Connor , Tobin Selene , Halliday Tanya , Prather Aric , Qeadan Fares
Published: May 2022

Introduction

The onset of the COVID-19 pandemic disrupted and changed sleep as well as elevated stress levels worldwide. Previous research has demonstrated a bidirectional relationship between stress and sleep, in that stress contributes to poorer sleep and poor sleep leads to higher stress. It is hypothesized that perseverative cognition (i.e., worry, racing thoughts) is a key cognitive mechanism in this relationship. The goal of our study was to examine the relationships between stress and sleep during a major global stressor, testing key cognitive and behavioral factors that may influence this relationship.

Methods

Adults aged 18 and above were recruited to complete a text-message survey twice per day for 3 weeks over a 4-month period. Sleep duration and efficiency during the previous night and evening/overnight perseverative cognition was measured in the morning survey, daily stress levels were measured in the evening survey. Physical activity was measured by the International Physical Activity Questionnaire (IPAQ). Results were analyzed using mixed effects models controlling for age, gender and race/ethnicity.

Results

Participants included 191 adults (91 F, mean age= 43, SD= 16 years). Results demonstrate that stress ratings were associated with higher sleep duration (p= 0.04) but perseverative cognition was associated with lower sleep duration and efficiency (p values <0.001). Participants who were more physically active had higher sleep duration (p=0.02) and efficiency (p< 0.001). Sleep did not predict next-day stress.

Conclusion

Results demonstrate that perseverative cognition is a key factor in the daily relationships between stress during the day and sleep at night. Higher physical activity was related to better sleep. These results indicate that interventions to reduce perseverative cognition may improve sleep during times of stress, including reducing the sleep-disrupting effects of the COVID-19 pandemic.

Support (If Any)

University of Utah i3 COVID-19 pilot grant, UL1TR002538
0239 A Latent Profile Analysis of actigraphic sleep and physical activity measures among Cambodian-Americans: Relationship with specific trauma symptoms
Authors: Master Lindsay , Wagner Julie , Feinn Richard , Scully Mary , Kuoch Theanvy , Kong Sengly , Berthold S , Buckley Thomas , Buxton Orfeu
Published: May 2022

Introduction

Sleep and physical activity are related to psychological trauma. Less is known about how individuals with distinct sleep and activity profiles differ on specific clusters of trauma symptoms. Cambodian-Americans who survived the Pol Pot genocide experienced severe collective trauma. This analysis explored group differences between sleep/activity profiles on specific trauma symptoms among Cambodian-Americans.

Methods

Participants in a diabetes prevention trial for Cambodian-Americans (NCT02502929) met inclusion criteria for depression and high diabetes risk (but did not have diabetes). They wore wrist actigraphy (sleep) and hip actigraphy (physical activity) for 7 days (≥3 days to be included) and completed the 16-item trauma symptom scale of the Harvard Trauma Questionnaire (HTQ; N=166). Latent Profile Analyses identified profiles using 3 mean actigraphic sleep and activity variables: total nightly sleep time, sleep maintenance efficiency, and minutes in moderate-vigorous physical activity. ANOVAs explored differences between sleep/activity profiles on the HTQ, specifically total scores and the “Avoidance/Numbing” and “Re-experiencing/Hyperarousal” subscales. Models were adjusted for psychotropic medication use.

Results

Participants were predominantly women (79%), mean age 55.3, with elevated trauma symptoms (17% were higher than 2.5 cutpoint; mean±SD= 1.90±0.61). Sleep and physical activity patterns yielded a BIC best fit with 3 sleep/activity profiles: Inactive Poor Sleepers (n=30, 18%), Highly Active Short Sleepers (n=35, 21%), and Moderately Active Good Sleepers (n=101, 61%). Differences were observed between profiles on the HTQ total score (p=0.03). Tukey’s post hoc test revealed that Inactive Poor Sleepers exhibited greater HTQ scores than Highly Active Short Sleepers (p<0.05), but did not differ from Moderately Active Good Sleepers. There was also a significant difference between profiles in the Avoidance/Numbing subscale (p=0.01); Inactive Poor Sleepers had higher Avoidance/Numbing than Highly Active Short Sleepers (p<0.05, Cohen’s d: 0.47). There were no differences between profiles on the Re-experiencing/Hyperarousal subscale (p=.09).

Conclusion

Individuals with contrasting actigraphic sleep/activity profiles differed on trauma symptoms. Inactive Poor Sleepers may benefit from specific interventions for Avoidance/Numbing symptoms. Future analyses will evaluate how changes in sleep/activity profiles are longitudinally related to psychological health and diabetes risk following interventions.

Support (If Any)

5R01DK103663
0244 Associations among pre-sleep arousal, facets of emotion dysregulation, and sleep quality and efficiency during pregnancy
Authors: Kaliush Parisa , Baron Kelly Glazer , Williams Paula , Conradt Elisabeth , Crowell Sheila
Published: May 2022

Introduction

Pre-sleep arousal contributes to over 50% of pregnant people experiencing poor sleep quality and clinical insomnia. Given that poor sleep during pregnancy is associated with depression and suicide ideation, there is a critical need to identify intervention targets, such as difficulties with emotion regulation. However, emotion dysregulation is multifaceted and warrants further investigation in sleep research, particularly during pregnancy. Thus, the present study examined associations among pre-sleep arousal, various dimensions of emotion dysregulation, and sleep outcomes during pregnancy.

Methods

Participants (N = 62; Mage = 29.89, SD = 4.19 years) were recruited during pregnancy and enrolled to represent a range of scores on the Difficulties in Emotion Regulation Scale. For 7 days during the 3rd trimester, they wore CamNtech MotionWatch-8 wrist actigraphs and completed the Pre-Sleep Arousal Scale and Consensus Sleep Diary.

Results

Hierarchical linear regression analyses indicated that high pre-sleep arousal predicted low sleep efficiency (measured via actigraphy) above and beyond facets of emotion dysregulation (B = -.316, p = .026). High pre-sleep arousal also predicted poor sleep quality (measured via diary) above and beyond total emotion dysregulation (B = -.038, p = .010). However, an interaction emerged between pre-sleep arousal and one particular facet of emotion dysregulation: access to emotion regulation strategies (B = -.005, p = .023). Specifically, high pre-sleep arousal was associated with poor sleep quality only among those who reported limited abilities to use emotion regulation strategies. Among participants who reported that they could access effective emotion regulation strategies, pre-sleep arousal did not predict sleep quality.

Conclusion

This study indicates that high pre-sleep arousal may not always be associated with poor sleep quality during pregnancy. Equipping pregnant people with emotion regulation strategies may buffer this association and promote long-term health. It is recommended that perinatal sleep scientists continue investigating relations between facets of emotion dysregulation and sleep.

Support (If Any)

This project was supported by the National Institute of Mental Health under Awards R01MH119070 (MPIs Crowell & Conradt) and F31MH124275 (PI Kaliush). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIMH.
0245 When do Emotions Impact Sleep? A State of the Evidence
Authors: Krizan Zlatan , Hisler Garrett , Boehm Nicholas
Published: May 2022

Introduction

While theories often highlight the bi-directional nature of sleep and emotional processes, the impact of emotions on sleep has been comparatively neglected. To appraise the state of evidence for the causal influence of emotions on sleep, a meta-analysis of the existing literature was conducted, alongside a diary study to estimate naturalistic effects.

Methods

First, a pre-registered meta-analysis using PRISMA guidelines evaluated the strength, form, and context of experimental effects of emotion inductions on sleep parameters (k=27). Quality of primary experiments was evaluated by independent raters, and theoretically-relevant features were extracted and examined as moderating factors of observed effects (i.e., sleep parameter, design, sleep context, types of emotion inductions and emotions). Random-effect models were used to aggregate effects for each parameter. Second, a complementary pre-registered diary study of young adults (N=89) tracked the links of their global emotions (reported separately in the evening and morning) with actiwatch-assessed sleep across two weeks (Nobs=1,188).

Results

First, across the meta-analyzed experiments, there was a significant impact of emotion inductions on delayed sleep onset latency (D=2.80 min, 95%CI 1.01, 4.52, g = .47), but no significant effects on other sleep parameters. While there was little evidence of publication bias, the studies overall were often of weak methodological quality and the typical study could only detect moderate-to-large impacts. There was also large heterogeneity pointing to substantive differences in effects. Second, multi-level regressions of sleep parameters on emotions reported in the evening from the diary study provided some evidence for delayed sleep on evenings with higher negative affect (b = .05, p < .10), with again no changes in other sleep parameters. Also, higher positive emotions predicted earlier and shorter sleep. The estimates were robust to accounting for emotions at the previous point.

Conclusion

These pre-registered investigations support the hypothesis that negative emotions delay sleep onset, but evidence regarding other sleep parameters was not conclusive. A diary study of real-life functioning partially replicated delayed sleep onset following more negative emotions, but the effect was modest. The results call for more targeted investigation that disambiguate distinct features of emotions and sleep.

Support (If Any)
0255 Sleep Hygiene Index: Associations with Sleep and Mental Health in College Students
Authors: Gibson Matelyn , Anderson Noah , Horton Alexis , Mullins Kayla , Reynolds Alexandria
Published: May 2022

Introduction

Typically, college students practice unhealthy sleep hygiene behaviors, obtain too little sleep, and experience poor sleep quality. Sleep hygiene includes the routines or practices that prepare a person for the best possible night of sleep. Good sleep hygiene habits, like creating a sleep-friendly environment and making time for sleep, promote healthy duration and quality of sleep. Stress is also an important factor to consider during the college experience. Sleep and mental health are tightly connected, and stress can negatively impact the sleep and mental health of individuals. The focus of the current study was to examine habitual sleep habits in college students, in association with sleep quality and psychological health.

Methods

Participants included 51 undergraduate students (18 men, average age M=20.25 years, SD=1.78) who wore wrist actigraphs to measure their typical sleep habits for one week. After one week, participants completed questionnaires about sleep quality (Pittsburgh Sleep Quality Index, PSQI) and sleep hygiene practices (Sleep Hygiene Index, SHI). Higher scores on PSQI represent poorer quality; higher scores on SHI represent unhealthy sleep hygiene behaviors. Mental health symptoms were measured by the Depression, Anxiety, and Stress Scale (DASS-21).

Results

Overall sleep duration was 6.59 hours and sleep efficiency was 82.55% as measured by actigraphy. PSQI scores (M=6.86) demonstrated poor sleep quality and SHI scores (M= 24.80) indicated overall poor sleep hygiene practices. SHI scores predicted higher PSQI scores (F(1, 50) = 18.05, p<.001), but did not predict sleep duration or efficiency. Depression, anxiety, and stress scores on the DASS predicted poorer sleep hygiene (F(1, 50) = 18.05, p<.001; F(1, 50) = 5.82, p=.020; F(1, 50) = 13.42, p<.001; respectively).

Conclusion

As expected, college students’ sleep was short in duration, poor in efficiency, and poor in quality. Additionally, poor sleep hygiene practices predicted poorer sleep quality. Interestingly, scores that indicated worse depression, anxiety, and stress predicted poorer sleep hygiene practices, suggesting that mental health may contribute to healthy sleep practices. More research is needed to understand the complex relationship between mental health, sleep, and healthy sleep practices

Support (If Any)

None.
0258 Systemic Determinants and Consequences of Sleep Disturbance in Family Members of the Critically Ill
Authors: Pignatiello Grant
Published: May 2022

Introduction

Family members of intensive care unit (ICU) patients are at increased risk of experiencing sleep disturbances, which in turn can exacerbate symptoms of anxiety and depression. However, little is known about how sociocultural determinants influence their predisposition to sleep disturbances, and how such sleep disturbances contribute to symptoms of anxiety and depression across the trajectory of the patient’s ICU stay. Therefore, we sought to: 1) identify individual and interpersonal sociocultural predictors of sleep disturbance symptoms, and 2) describe the influence of select sociocultural determinants and sleep disturbances on subsequent symptoms of anxiety and depression.

Methods

Using a repeated-measures, correlational design, we recruited family members of incapacitated, mechanically ventilated patients within four adult intensive care units at a tertiary medical center in northeast Ohio. We collected baseline data (T1) after obtaining informed consent and seven (T2) days post-baseline. We measured individual and interpersonal sociocultural determinants, as well as symptoms of sleep disturbance, anxiety, and depression with self-report instruments. To test our aims, we used step-wise linear regression.

Results

For aim 1 (N = 30), participants who were female, non-white, married, and had prior healthcare decision-making experience reported more severe sleep disturbance symptoms (R2 = .69, F(11,18) = 3.59, p = .008). For aim 2 (n = 20), the aforementioned determinants and T1 sleep disturbance scores significantly predicted T2 anxiety (R2 = .62, F(5,14) = 4.61, p = .01) and depression (R2 = .65, F(5,14) = 5.24, p = .006) severity. None of the predictors were statistically significant for the full anxiety model; notably, the effects of gender and prior healthcare decision-making experience were statistically significant until T1 sleep disturbance was inserted in the model. T1 sleep disturbance scores were the only statistically significant predictor in the full depression model.

Conclusion

We provide preliminary evidence that sleep disturbances among family members of ICU patient may contribute to the severity of depressive symptoms in family members of ICU patients. We encourage future researchers to replicate and expand upon these findings to understand the development of sleep disparities in this vulnerable population.

Support (If Any)

Sayre Memorial Fund; Midwest Nursing Research Society; NCATS (1KL2TR002547)
0270 EEG Slow Wave Coherence is Related to PSG-Derived Measures of Sleep Fragmentation and Cognition in the WashU BASE Cohort
Authors: Milman Noah , Reynolds Christina , Balba Nadir , Ju Yo-El , Lim Miranda
Published: May 2022

Introduction

There exists a bidirectional relationship between sleep disruption and neuropathology in Alzheimer’s disease (AD). Electroencephalogram (EEG) during polysomnography (PSG) provides an opportunity to examine stereotyped, coordinated brain activity. Specifically, slow wave activity (SWA), a defining feature of non-REM sleep, is aberrant in AD, and disruption of SWA in healthy adults is related to increased amyloid-beta levels. Coherence of slow waves across different cortical regions may serve as a metric of brain network coordination, sensitive to earliest AD pathology. We explored slow wave coherence during sleep in relation to sleep macrostructure and cognitive performance in the Biomarkers of Alzheimer’s Disease in Sleep and EEG(BASE) cohort.

Methods

EEG was collected during an attended overnight PSG from the BASE cohort (n=79, average age = 70.8 ± 4.4 years), approximately 20% of whom had Clinical Dementia Rating (CDR) of 0.5-1. A custom slow wave peak detector was implemented in MATLAB to across 6 EEG leads (C3, C4, F3, F4, O1, and O2), and slow wavecoherence was calculated as the proportion of sleep with slow waves occurring within 100ms in all 6 leads.

Results

EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM. Slow wave coherence in N2/N3 was negatively correlated with measures of sleep fragmentation (wake after sleep onset Spearman r = -0.3, p 0.008; number of awakenings r = -0.39, p 0.0004), and positively correlated with sleep efficiency (r = 0.28, p = 0.013). Slow wave coherence in N2/N3 was correlated with overall cognition as measured by MoCA adjusted z-score (r = 0.2, p 0.087) and executive function as measured by Trailmaking B adjusted z-score (r = 0.25, p 0.038).

Conclusion

Slow wave coherence was strongly correlated with measures of sleep quality. Reduced slow wave coherence was associated with poorer cognitive executive function. EEG slow wave coherence is a novel, promising approach to measure coordinated brain network function that is sensitive to early cognitive dysfunction in AD.

Support (If Any)

NIH R01 AG059507, ARCS Foundation Scholar
0307 Is Poor Sleep Associated with Use of Multiple Benzodiazepine Receptor Agonists in Older Veterans?
Authors: Ghadimi Sara , Alessi Cathy , Kelly Monica , Martin Jennifer , Moore Alison , Grinberg Austin , Zeidler Michelle , Dzierzewski Joseph , Mitchell Michael , Guzman Andrew , Armendariz Jessica , Badr Safwan , Fung Constance
Published: May 2022

Introduction

Benzodiazepine receptor agonists (BZAs) are often prescribed for insomnia in older adults. Polypharmacy increases risk of adverse events in this population in general, particularly when medications within the same class are prescribed. During an ongoing hypnotic deprescribing trial, we explored use of more than one BZA (multi-BZA use) and self-reported sleep quality among older adults.

Methods

Telephone surveys were performed for recruitment to an ongoing BZA deprescribing trial. Participants aged ≥ 55 years who were prescribed at least one BZA (i.e., alprazolam, clonazepam, lorazepam, temazepam, or zolpidem) from a Department of Veterans Affairs pharmacy in Southern California were asked about their use of each BZA over the past 3 months for sleep (yes/no). Multi-BZA use was defined as using > 1 different BZA over the past 3 months. Self-reported sleep items included duration of sleep problems (<3, 3-12, or >12 months), sleep quality (very or fairly good, fairly or very bad), and sleep efficiency (mean total sleep time over time in bed). Analyses compared sleep variables between multi-BZA and non-multi-BZA users (Fisher’s Exact or t-tests).

Results

Among participants (N=359), 152 (42.3%) reported using zolpidem, 41 (11.4%) lorazepam, 39 (10.9%) alprazolam, 31 (8.6%) clonazepam, and 29 (8.1%) temazepam during the past 3 months. 35 (9.8%) participants reported taking more than one of these drugs. 93.9% reported their sleep problems were present for ≥3 months. 68.3% of participants reported their sleep was fairly/very bad, and mean sleep efficiency was 67.9 (SD 18.5). There were no significant differences between multi-BZA versus non-multi-BZA users in duration of sleep complaints (Fisher’s Exact=1.0; p=.842), sleep quality (Fisher’s Exact=0.70; p=0.56) or sleep efficiency (p=0.91).

Conclusion

We found 1 in 10 older adults prescribed a BZA for sleep reported multi-BZA use over the past 3 months. Multi-BZA use was unrelated to duration of sleep complaints, sleep quality or sleep efficiency. Whether the use was simultaneous or staggered, these findings are concerning, given the elimination half-life of most of the BZAs and that polypharmacy, especially within medication class, may increase risk of adverse events (e.g., falls). Further research is needed to explore factors contributing to multi-BZA use.

Support (If Any)

NIA R01AG057929, VA IIR 17-234
0309 A multidisciplinary initiative for screening and treating sleep disorders in menopause: experience in a tertiary center
Authors: Garza-Marichalar Jose , Chávez-Luévanos Beatriz , Hernández-Sánchez Elisa , Ferrari-Aquino Ananké , Garza-Leal Jose , Castillo-Saenz Lorena , Sánchez-Gutiérrez Paola , Parra-Mendoza Itzia
Published: May 2022

Introduction

Menopause is a time of adaptation and transition to a new biological situation that implies the loss of reproductive function. The onset of menopause coincides with the onset of other symptoms related to hypertension, weight gain, hormonal changes, osteoporosis, and vascular disease. These hormonal changes could be responsible for some sleep disorders. It is well documented that the prevalence of sleep disorders is higher in post-menopause, regardless of other factors such as aging.

Methods

As part of routine care for patients in a gynecology outpatient clinic, all patients with menopause were screened for sleep disorders using the following clinical tools: the Pittsburgh Sleep Quality Index (PSQI), the Patient Health Questionnaire (PHQ-9), the STOP-BANG Questionnaire, and the Epworth Scale.A full clinical history was also taken, focusing on sleep habits and risk factors that could contribute to sleep difficulties.Patients that presented with any sleep disorders were assessed and continued their treatment depending on these results.

Results

Between August 2021 and December 2021, a total of 60 patients going through menopause that attended the gynecology outpatient clinic for other reasons were screened for sleep disorders.Using the PSQI we identified 51 patients with low quality sleep (defined as a score of 5 or higher using this tool).Depression was also identified in a total of 43 patients, none of which were receiving treatment. All these patients also reported low-quality sleep and other problems such as hypersomnia, which in turn was identified in 15 patients using the Epworth Scale.Thirty-five patients were identified as having at least 3 risks factors for obstructive sleep apnea with the STOP-BANG questionnaire and were ordered a polysomnography.

Conclusion

Sleep disorders are highly prevalent in menopause. With this initiative, we present our experience and results incorporating routine screening for these disorders in a clinical setting outside of a sleep clinic.Integrating this information can be helpful for working in multidisciplinary teams aimed at reducing chronic diseases and mortality in these patients. This is a multidisciplinary approach that will continue in our institution.

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0336 Sleep Time and Risk Taking Behaviour in Night Shift-Workers
Authors: Ravi Anushree , Bryans Helena , Ruprich Melissa , Burgeess Helen , Roth Thomas , Drake Christopher , Cheng Philip
Published: May 2022

Introduction

Night shift-workers are integral in a multitude of industries. Many night shift-workers experience sleep loss, which may impact their risk taking behaviour.This study aimed to identify the correlation between total sleep time (TST) and shift-workers' engagement in risk-taking behavior.

Methods

53 night shift-workers participated in the experiment. Polysomnography was conducted to determine the TST of shift-workers prior to completing a task that measured risk taking behaviour. This task simulated a driving scenario in which participants must decide whether or not to proceed at a yellow traffic light. Participants were not informed of the duration of the yellow light, which varied unpredictably in duration. The task consisted of 16 trials, and participants earned points that translated to a cash incentive (25 points = 25 cents). Participants either earned or lost 25 points depending on the success of each trial. Failure was defined as running a red light. Those who proceeded at every trial were categorized as insensitive to risk.

Results

The data revealed that those insensitive to risk had a significantly lower TST value.Those insensitive to risk had a lower mean sleep time (374 minutes, SD=75) compared to those sensitive to risk (415 minutes, SD= 42), p =.01 (Cohen’s d=0.67).

Conclusion

Sleep time is associated with risk-taking behavior in night shift-workers. This study offers insight into the amplification of risk taking behaviours within night shift-workers posed by sleep loss, possibly leading to errors and injuries in the workplace. This association may also suggest that implementation of measures to increase sleep for night shift-workers could decrease risk-taking behaviours.

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K23HL133186
0351 Towards Interpreting Consumer Sleep Data: Distributions of Sleep Scores
Authors: Raymann Roy , Nayak Nyayabrata , Watson Nathaniel , Gahan Luke , Gottlieb Elie
Published: May 2022

Introduction

With the rise of sleep measurement technology becoming widely available to the public, it has become apparent that traditional sleep metrics might not be best suited for a lay audience. Most consumer industry has started including a metric that would capture sleep quality, although the exact calculations of these scores remain proprietary. These novel outcome metrics require a set of reference values in order to become interpretable. Here, we provide reference values for the parameters SleepScore, BodyScore and MindScore as included in the SleepScore Labs non-contact radiofrequency sleep measurement devices.

Methods

SleepScore is a sleep quality metric that includes objectively measured total sleep time (TST), sleep onset latency (SOL) and sleep stage durations, normalized for aged and sex, using reference values of Ohayon et al (2004), ranging from 0-100. BodyScore reflects the normalized amount of deep sleep, whereas MindScore reflects the normalized amount of REM, ranging from 0-100. Data from 40,862 S+ and Max users between 18 and 98 years old were used to calculate distribution statistics.

Results

The average age of users was 53±15 years old. Individual scores of SleepScore, BodyScore and MindScore ranged from 0-100 and their distribution was left-skewed. SleepScore averaged 81±11, with the first quartile (Q1) at 73, median at 81 and third quartile (Q3) at 88, and a mode of 89. BodyScore averaged 81±10 with Q1 at 73, median at 80 and Q3 at 86, and a mode of 84. MindScore averaged 78±10 with Q1 at 72, median at 79 and Q3 at 84, and a mode of 83. Despite being algorithmically normalized for age, average SleepScore increased from 70 to 88 across the age range, BodyScore increased from 71 to 89, and MindScore increased from 75 to 81.

Conclusion

SleepScores, BodyScores and MindScores presented to the average consumer will mostly show them a number in the low 70 to high 80 range. This distribution was intentionally created as being left-skewed to prevent triggering anxiety that may contribute to orthosomnia. Despite the intent to create a normalized score that would not be impacted by age, the data show an increase of scores by age.

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0364 GOOD Sleep Care: Geospatial Optimization of Distant Sleep Care in the VA
Authors: Atwood Charles , Smith Conor , Totten Annette , Boudreau Eilis , Sarmiento Kathleen , Hahn Zachary , Folmer Robert , Tock Jamie , Hotchkiss John
Published: May 2022

Introduction

Obstructive Sleep Apnea (OSA) is very prevalent in the Veterans Administrations (VA) clinical population, affecting about 24% of Veterans. Sleep medicine services in VA suffers from uneven distribution, which especially affects Veterans in rural populations, where care is often not available except by long distance travel. Travel burden is increasingly recognized as a burden on Veteran care that can be ameliorated by telemedicine. Little is known about travel burden and few attempts have been made to quantify it. We have attempted to quantify travel burden for rural Veterans using data from VA's Office of Rural Health supported Enterprise Wide Initiative for Telesleep Medicine. In this project we focus on travel from a Veteran's home to a facility-based sleep clinic visit (traditional face-face visit) vs. how much travel would be avoided if visits were performed using video-based telemedicine with the Veteran at his/her residence.

Methods

Purpose built geocoding software (validated in prior studies) was used to quantify the driving distance from the Veterans home zipcode to the zipcode of the facility where the visits would were performed. The data were taken from the VA's national corporate data warehouse (CDW). Over 4 million sleep encounters were coded. CPT4 codes were used to define the clinic visit encounters from 2016 through 2020. We set the travel range where telemedicine is reasonable based on distance at greater than 40 miles from the VA medical center which is how VA currently defines "distant travel".

Results

No. of Encounters (thous) 2016, 557; 2017, 623; 2018, 678; 2019, 667; 2020, 420Miles ObsTravel (mil) 2016, 38; 2017, 42; 2018, 45; 2019, 43; 2020, 27 Miles Travel if telemed used (mil) 2016, 25; 2017, 27; 2018, 29; 2019, 27: 2020, 17Difference (mil miles) 2016, 13; 2017, 15; 2018, 16; 2019, 16 2020, 10Between 10 and 16 million miles could be saved each year for sleep medicine visits if all travel > 40 miles was converted to video home visits.

Conclusion

Travel burden in this analysis, defined by miles driven to and from a sleep medicine encounter, could be dramatically reduced if telemedicine visits were used for sleep apnea evaluation and management. This reduction in travel equates to a reduction in automobile related use-costs, lower carbon footprint, fewer automobile crashes with injury and death. Assuming that clinical effectiveness of home telemedicine is comparable to traditional visits, these data support the vigorous adoption of telemedicine for sleep medicine services.

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VA Office of Rural Health; Measurement QUERI, San Francisco VA Medical
0379 At-home Sample Collection for Identification of Alterations in HPA-axis Activity and Cortisol Area Under the Curve
Authors: Kreitzberg Daniel , Gavin Kathleen , Daumeyer Natalie , Seixas Azizi , Bauer Timothy
Published: May 2022

Introduction

Sleep problems are associated with alterations in diurnal cortisol patterns, reflective of altered hypothalamic-pituitary-axis (HPA) activity. At-home collection of dried samples for measurement of urinary free cortisol are useful for assessment of diurnal rhythms because they do not impose the stress or sleep disruption associated with inpatient studies. The purpose of this analysis was to assess urinary cortisol responses among a large real-world dataset of at-home collection kit users and the potential for utility in identifying alterations in HPA-axis activity.

Methods

This retrospective analysis evaluated data from 3,966 individuals who used a Sleep and Stress Test (Everlywell, Inc.) between September 2017 and July 2021. Four dried urine spot samples were collected according to habitual sleep patterns: upon waking (T1), two hours after waking (T2), prior to the evening meal (T3), and at bedtime (T4). Urinary free cortisol normalized to creatinine, indicative of HPA-axis activity was assessed (area under the curve (AUC), wake to bedtime diurnal cortisol slope (DCS), mid-morning (T2) and bedtime (T4) cortisol levels). Sample reference ranges were established from two standard deviations above and below the log-transformed mean at each time point, values were reverted to original units for reporting.

Results

The sample was predominantly female (n=2,832, 71%). Mean age was 42.8 (sd=12.0) years. Seventy-eight individuals had mid-morning (T2) cortisol levels that were higher than the sample reference range (7.0-170.7 ug/g Cr), and 88 individuals below. Bedtime (T4) cortisol levels were elevated in 145 individuals (reference range 1.1-25.0 ug/g Cr). Total AUC for cortisol was high in 93 and low in 81 of individuals. The slope of the diurnal cortisol response was flattened in 20.6% (n=816) of the sample.

Conclusion

This real-world study demonstrates that at-home dried urine sample collection can be used to characterize normal and atypical HPA-axis activity. Convenient assessment of alterations in diurnal cortisol patterns are critical in identifying and understanding the mechanisms of HPA-axis activity common in sleep disruptions/disorders. Future studies utilizing this methodology may prove useful in identifying subpopulations with altered HPA-axis activity, sleep issues, and potential relationships to cardiometabolic health.

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