0120 Association Between Sleep Duration and Daytime Memory and Cognition Depends on Sleep Quality: Data from the 2017 Israel Social Survey

Introduction

This study examines the relationship between sleep duration, sleep disturbance, and cognitive problems in a representative sample of the Israeli population.

Methods

7,230 Israelis responded to an Israeli Bureau of Statistics population-based survey of households from the year 2017. All variables were self-reported. Outcome of interest was difficulty with memory/concentration (none, mild, or severe). Predictors included previous month sleep duration (<=5hrs, 6hrs, 7hrs [reference], 8hrs, or >=9hrs) and sleep disturbance (none [reference], mild [1/week], moderate [2–3/week], or severe [>3/week]). Covariates included age, sex, ethnic group, and financial status. Multinomial logistic regressions evaluated the relationships between variables, and post-hoc testing identified relationships within specific subgroups.

Results

72.9% denied cognitive problems, 22.2% reported mild problems, and 4.9% severe problems. In adjusted analyses, Sleep <=5hrs and >=9hrs were associated with mild (RRR=1.39, p<0.0005), (RRR=1.46, p=0.004) and severe (RRR=2.75, p<0.0005), (RRR=3.24, p<0.0005) cognitive problems, respectively. Mild, moderate, and severe sleep difficulties were associated with mild cognitive problems (RRR=2.09, p<0.0005), (RRR=2.22, p<0.0005), (RRR=2.44, p<0.0005), and severe cognitive problems (RRR=1.77, p=0.001), (RRR=3.04, p<0.0005), (RRR=4.22, p<0.0005), respectively. There was an interaction between sleep duration and sleep difficulties (p<0.05). Among those denying sleep difficulties, only >=9hrs of sleep was associated with cognitive problems. Among those with mild, moderate, and severe sleep difficulties, both short and long sleep were associated with cognitive problems.

Conclusion

In an Israeli population sample, both sleep duration and quality were associated with cognitive problems. Among those with sleep difficulties, short and long sleep duration were associated with cognitive problems, but among those denying sleep difficulties, only long sleep was associated with cognitive problems. These results suggest that the impact of sleep loss on real-world cognition may also rely on the presence of poor sleep quality.

Support

Dr. Grandner is supported by R01MD011600

0553 Hallucinogen Use Among College and University Students: Associations with Insufficient Sleep and Insomnia

Introduction

Previous studies have shown that poor sleep is associated with alcohol use, smoking, and other substance use, especially among young adults. Yet, very little is known about hallucinogen use.

Methods

Data from the 2011-2014 National College Health Assessment were used (N=113,749), representing a wide range of students across the US. Hallucinogen use was reported as “never,” “past,” and “present” (reflecting use in the past 30 days). Students also self-reported nights/week they did not get enough sleep to feel rested (insufficient sleep), as well as nights/week they had difficulty falling asleep (initial insomnia). Responses for both were categorized as 0, 1-2, 3-4, 5-6, or 7 nights/week. Multinomial logistic regressions examined hallucinogen use as outcome (past or present vs never) and sleep as predictor, with adjustment for covariates (age, sex, race/ethnicity, and survey year) and mental health (past 30 days depression/anxiety).

Results

Hallucinogen use was infrequently reported, with 4.8% (N=5,493) reporting past use and 0.98% (N=1,119) reporting present use. In adjusted analyses, increase likelihood of past use was associated with insufficient sleep on 1-2 (RRR=1.28, p=0.001), 3-4 (RRR=1.37, p<0.0005), 5-6 (RRR=1.30, p<0.0005), and 7 (RRR=1.34, p<0.0005) nights per week, as well as 1-2 (RRR=1.30, p<0.0005), 3-4 (RRR=1.52, p<0.0005), 5-6 (RRR=1.58, p<0.0005), and 7 (RRR=1.49, p<0.0005) nights per week of initial insomnia. Present use was associated with 1-2 (RRR=1.44, p<0.0005), 3-4 (RRR=1.76, p<0.0005), 5-6 (RRR=2.05, p<0.0005), and 7 (RRR=1.83, p<0.0005) nights per week of initial insomnia. When mental health was entered into the model, results were maintained.

Conclusion

Past use of hallucinogens was associated with insufficient sleep as well as insomnia. Present use was also associated with insomnia. When mental health was included in models, all results were maintained. It is not clear whether hallucinogen use leads to, or is predicted by, sleep difficulties.

Support

Dr. Grandner is supported by R01MD011600

0374 Decline in Habitual Sleep Duration Over 10 Years and Worsening Sleep Disparities: Data From NHIS (2006-2015)

Introduction

Despite claims in the media, evidence that habitual sleep has declined in recent years is scant. Few data sources exist that systematically document sleep duration in a nationally representative sample, in the same way, over several years.

Methods

Data from 10 years of the National Health Interview Survey were used (N=305,555). During all years, habitual sleep duration, age, sex, race/ethnicity, and height/weight were recorded in the same way. Weighted regression analyses examined sleep duration as the outcome, year as linear predictor, and sociodemographics as covariates. Then, interaction terms examined whether the linear change associated with years was differentially experienced by different sociodemographic groups.

Results

The linear trend of sleep duration over the past 10 years is a loss of 0.78 minutes per year (95%CI -0.91,-0.64; p<0.0001). After adjustment for age, sex, race/ethnicity and BMI, this remained relatively unchanged at 0.86 minutes (95%CI -0.99,-0.73; p<0.0001). A year-by-race/ethnicity interaction was observed (p<0.05). In stratified analyses, Non-Hispanic Whites showed a loss of 0.68 minutes per year (95%CI -0.84,-0.52, p<0.0001). This was 1.33 minutes/year in Blacks/African-Americans (95%CI -1.74,-0.92; p<0.0001), 1.57 minutes/year in Mexican-Americans (95%CI -1.98,-1.16; p<0.0001), 0.99 minutes/year in other Hispanics/Latinos (95%CI -1.51,-0.47; p<0.0001), 0.74 minutes/year in Asians (95%CI -1.24,-0.25; p=0.003), and 1.80 minutes/year in American Indians/Alaskan Natives (95%CI -3.57,-0.03, p=0.046).

Conclusion

On average, the US population has lost 47 seconds of nightly sleep per year over a 10-year period, equating to about 4.7 hours of sleep per year, but racial/ethnic groups were impacted differently. Compared to Non-Hispanic Whites, Blacks/African-Americans lost 96% more sleep, Mexicans lost 131% more sleep, other Hispanics/Latinos lost 46% more sleep, Asians lost 9% more sleep, and American Indians lost 165% more sleep. Thus, sleep disparities may be widening.

Support

Dr. Grandner is supported by R01MD011600

1108 Associations Between Insomnia And Anxiety Symptoms: Which Elements Of Insomnia Are Associated With Which Elements Of Anxiety?

Introduction

It is still not clear which aspects of insomnia are associated with various aspects of anxiety problems. Knowing this could better guide treatment of insomnia comorbid with anxiety.

Methods

Data from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study were used, including N=1003 adults age 22-60. All participants completed the Insomnia Severity Index (ISI) and the GAD7 anxiety questionnaire. The ISI was divided into 3 sections, based on prior work: SLEEP symptoms (difficulty sleeping), DAYTIME symptoms (difficulty functioning), and PERCEPTION symptoms (dissatisfaction). GAD7 items included anxiety level, loss of control, worry about many things, difficulty relaxing, restlessness, irritability, and fear. Logistic regression analyses examined each symptom, with each component of the ISI as predictor, as well as age, sex, race/ethnicity and education as covariates.

Results

SLEEP symptoms were independently associated with control (OR=1.09, p=0.03), many worries (OR=1.1, p=0.017), restlessness (OR=1.1, p=0.009), and irritability (OR=1.1, p=0.04). DAYTIME symptoms were independently associated with anxiety level (OR=1.3, p<0.0005), control (OR=1.2, p<0.0005), many worries (OR=1.3, p<0.0005), difficulty relaxing (OR=1.2, p=0.004), restlessness (OR=1.3, p=0.001), and irritability (OR=1.2, p<0.0005). PERCEPTION symptoms were uniquely, independently associated with anxiety level (OR=1.1, p=0.03), control (OR=1.2, p=0.001), many worries (OR=1.2, p=0.001), difficulty relaxing (OR=1.4, p<0.0005), irritability (OR=1.2, p=0.018), and feelings of fear (OR=1.2, p=0.002).

Conclusion

The DAYTIME and PERCEPTION symptoms of insomnia were strongly related to anxiety symptoms. Current treatments for insomnia focus mainly on improving sleep. Future research should test the hypothesis that treating daytime symptoms of insomnia may aid patients with comorbid anxiety.

Support

The SHADES study was funded by R21ES022931. Dr. Grandner is supported by R01MD011600.

0376 Demographic and Socioeconomic Implications of Excessive Daytime Sleepiness in the Community

Introduction

Daytime sleepiness impairs daily functioning and may be directly related to insufficient nighttime sleep. Previous studies have assessed disparities in sleep duration and quality, but community-level disparities in daytime sleepiness using validated measures are lacking.

Methods

Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22-60. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Predictors included age, sex, race/ethnicity, education, and social class (“Upper middle class or above,” “Middle class,” “Lower middle class,” “Poor,” or “Very Poor”). One-way ANOVAs evaluated group differences. Stepwise linear modeling evaluated ESS score relative to sociodemographic predictors. Final models included all variables entered together to evaluate independent effects. Finally, habitual sleep duration was entered as an additional covariate.

Results

ESS score was higher among racial/ethnic minorities (p=0.0006), men (p<0.0001), those with less education (p=0.008) and lower social class (p=0.0007), and those who are retired or unable to work (p=0.03); marginal differences were seen according to age (p=0.06). Using a model-building approach, age, sex, race/ethnicity, education, social class, and employment were evaluated. Only race/ethnicity (F=5.1, p=0.0004), education (F=4.8, p=0.003), and social class (F=2.14, p=0.046) incrementally added variance to model R2. No 2-way interactions were found. In the final model, significant predictors included Black/African-American race/ethnicity (B=0.94, p=0.01), some college (B=0.99, p=0.005), and being very poor (B=2.16, p=0.005). When controlling for nocturnal sleep duration, the increased sleepiness associated with being Black/African was attenuated (p=0.06), but the other relationships were still significant.

Conclusion

There is a “sleepiness disparity” in the population associated with race/ethnicity and socioeconomics. Daytime sleepiness in the community is associated with being Black/African-American, having some college, and being “very poor.” The race/ethnicity difference in daytime sleepiness may be partially explained by differences in total sleep time.

Support

This work was supported by a grant from Jazz Pharmaceuticals. The SHADES study was funded by R21ES022931. Dr. Grandner is supported by R01MD011600.

0406 Daytime Sleepiness in The Community: Implications for Sleep, Circadian, and Physical Health

Introduction

Daytime sleepiness impacts performance and well-being. The present study used validated measures to explore associations of community-level daytime sleepiness with sleep health, preferred sleep phase, physical inactivity, and overall health.

Methods

Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22-60 from the community. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Outcomes of interest included the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), endorsement of a preference for an advanced or delayed sleep phase on the Sleep Disorders Symptom Check List (SDSCL), STOP-BANG sleep apnea questionnaire score, sedentary time assessed with the International Physical Activity Questionnaire (IPAQ), and the global health item on the SF-12, operationalized as excellent/good or fair/poor health. Through regression analyses, we assessed whether daytime sleepiness was independently associated with several sleep, circadian and physical health outcomes, adjusting for habitual sleep duration and sociodemographic factors like age, sex, education, and race/ethnicity.

Results

Our adjusted models indicate that daytime sleepiness was associated with insomnia (B=0.57; 95%CI: 0.50, 0.65; p<0.0001), sleep quality (B=0.34; 95%CI: 0.29, 0.39; p<0.0001), advanced sleep phase (OR=1.06; 95%CI: 1.03, 1.09; p<0.0001), delayed sleep phase (OR=1.05; 95%CI: 1.02, 1.07; p=0.0003), STOP-BANG score (B=0.08; 95%CI: 0.07, 0.10; p<0.0001), sedentary minutes (B=6.12; 95%CI: 2.77, 9.47; p=0.0004), and overall poor health (OR=1.10; 95%CI: 1.07, 1.13; p<0.0001). After additional adjustment for habitual sleep duration, all relationships were maintained.

Conclusion

Daytime sleepiness is associated with more severe insomnia, preference for advanced or delayed sleep timing, worse sleep quality, and greater risk of sleep apnea. Moreover, daytime sleepiness was associated with greater sedentary time and worse overall health. Since these relationships are independent of sleep duration, they likely do not reflect an effect of sleep deprivation.

Support

This work was supported by a grant from Jazz Pharmaceuticals. The SHADES study was funded by R21ES022931. Dr. Grandner is supported by R01MD011600.

0243 Community-Level Daytime Sleepiness and Substance Use: Implications of Sleep Time and Mental Health

Introduction

Daytime sleepiness is associated with impaired functioning and well-being. Those with more sleepiness may turn to illicit substances to overcome these problems. The present study examined whether community-level daytime sleepiness is associated with the likelihood of drug use.

Methods

Data were pulled from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 community adults (age 22–60). Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Use of different substances was assessed with the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). The present analyses examined use within the past month of alcohol, tobacco, cannabis, cocaine, amphetamines, inhalants, sedatives, hallucinogens, and illicit opioids. A separate item assessed caffeine. Ordinal logistic regression analyzed ESS score as a predictor of frequency of substance use adjusted for age, sex, education, and race/ethnicity. Additional models included habitual sleep duration and score on the PHQ9 depression scale.

Results

In sociodemographically-adjusted analyses, ESS score was associated with an increased risk of using tobacco (OR=1.04, p=0.015), cannabis (OR=1.04, p=0.014), cocaine (OR=1.07, p=0.009), amphetamines (OR=1.06, p=0.025), inhalants (OR=1.13, p=0.002), sedatives (OR=1.07, p=0.003), hallucinogens (OR=1.12, p=0.001), and opioids (OR=1.12, p=0.0001). Controlling for sleep duration did not significantly affect these relationships, while controlling for depression made every association non-significant except hallucinogens (OR=1.09, p=0.040).

Conclusion

Daytime sleepiness was associated with increased use of nearly all drug categories, but not alcohol or caffeine. Public consumption of alcohol and caffeine might be sufficiently common that the presence of their use cannot be adequately associated with sleepiness. Moreover, the increased frequency of drug use with sleepiness is not linked to sleep deprivation but may reflect emotional distress.

Support

This work was supported by a grant from Jazz Pharmaceuticals

Dr. Grandner is supported by R01MD011600

0013 Mobile Device Use in Bed and Relationships to Work Productivity: Impact of Anxiety

Introduction

Mobile phone use at night is associated with worse sleep quality. It may also be associated with daytime productivity, possibly via anxiety.

Methods

Data were obtained from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study, including N=1007 adults age 22–60. Mobile device use in bed was assessed as the frequency that participants reported: a device in the bedroom, use of the device in bed, texting, emails, internet browsing, calls, and/or social networking in bed, being woken up by the device in a planned (alarm) or unplanned (alert/call/message) way, and checking the phone at night. Each of these were coded as “never,” “rarely,” or “often.” Work productivity was assessed with the Well-Being Assessment of Productivity (WBA-P; scores 0–22 measure productivity loss). Regressions with WBA-P score as outcome and mobile phone variables as predictors were adjusted for age, sex, race/ethnicity, education, and income level. Post-hoc analyses included GAD7 score to examine the mediating role of anxiety.

Results

The presence of a device was not associated with productivity loss, but frequent use (“often”) was (B=1.26,p=0.01). Increased productivity loss was also seen in those who frequently (“often”) sent texts (B=1.20,p=0.008), browsed internet (B=1.14,p=0.01), emailed (B=2.09,p<0.0005), called (B=1.42,p=0.004), and used social media (B=1.26,p=0.004). Productivity loss was associated with being woken by a call/alert “rarely” (B=1.20,p=0.001) or “often” (B=1.72,p=0.005), but not by alarm. Checking the phone at night “rarely” (B=0.89,p=0.01) and “often” (B=1.73,p<0.0005) were also associated with productivity loss. When anxiety was entered into the model, all relationships except those with frequent emails and calls in bed became nonsignificant.

Conclusion

Anxiety may be the underlying cause for both increased mobile phone usage and reduced productivity. Reducing anxiety levels may indirectly aid in decreasing nighttime mobile phone use and increasing daytime productivity.

Support

The SHADES study was funded by R21ES022931

Dr. Grandner is supported by R01MD011600

0241 Habitual Daytime Sleepiness and the Timing of Use of Alcohol, Tobacco, and Caffeine

Introduction

Alcohol, caffeine, and tobacco are frequently used in the community, and the timing of use may impact daytime sleepiness. The present analysis examined relationships between daytime sleepiness and timing of alcohol, tobacco, and caffeine use in a real-world sample.

Methods

Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22–60 from the community. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Participants were asked if they had ever used caffeine, alcohol and tobacco. If they reported using a substance, they were then asked which times of day they were likely to use the substance: 5AM-8AM, 8AM-11AM, 11AM-2PM, 2PM-5PM, 5PM-8PM, 8PM-11PM, 11PM-2AM, and 2AM-5AM. Logistic regression analyses examined the relationship between ESS score and likelihood of use of substances at each time.

Results

ESS score was associated with increased odds of ever using alcohol (OR: 1.05, 95% CI: 1.01 to 1.09) or tobacco (OR: 1.04, 95% CI: 1.01 to 1.07). ESS score was associated with an increased likelihood of drinking alcohol in the morning (5AM-8AM, OR: 1.13) and night (11PM-5AM, OR: 1.05). Sleepiness was also associated with increased likelihood of tobacco use in the afternoon (11AM-2PM, OR 1.04) and night (11PM-2AM, OR 1.05). Finally, ESS score was associated with increased likelihood of caffeine use during the midday and afternoon (11AM-5PM, OR: 1.04).

Conclusion

Greater sleepiness is associated with use of alcohol in the morning and at night, and with use of tobacco in the afternoon and at night. Finally, increased sleepiness was associated with caffeine use during the latter part of the workday. Some of these use patterns may be a cause of sleepiness (e.g., morning alcohol use or nighttime smoking) and some a consequence (e.g., daytime caffeine and tobacco use). More research on the impact of real-world sleepiness on real-world substance use is warranted.

Support

This work was supported by a grant from Jazz Pharmaceuticals

Dr. Grandner is supported by R01MD011600The SHADES study was funded by R21ES022931

1053 Sleep Duration And Timing Associated With History Of Breast Prostate And Skin Cancer: Data From A Nationally-representative Sample

Introduction

Sleep disturbances are a common problem among cancer survivors. Also, cancer patients can have altered circadian rhythms and these changes can continue to affect the patient long after the conclusion of their treatment. This analysis aims to investigate how the sleep and wake times of cancer survivors differ from the rest of the population, depending on the type of cancer.

Methods

Data from the 2015-2016 National Health and Nutrition Examination Survey were used. Population-weighted data on N=5,581 individuals provided complete data. History of breast, prostate, and skin cancer (melanoma or other) was self-reported. Sleep duration was self-reported in half-hour increments, and typical bedtime and waketime was self-reported. Covariates included age, sex, and race/ethnicity. Weighted linear regressions with sleep duration, bedtime and waketime were examined, with each cancer type as predictor.

Results

Prevalence was 1.7% for prostate cancer, 1.5% for breast cancer, 2.3% for non-melanoma skin cancer, and 0.8% for melanoma. In adjusted analyses, prostate cancer was associated with an additional 26.5 minutes of average total sleep (95%CI 2.2,50.9, p=0.03), a 23.1 bedtime minutes earlier (95%CI -40.4,-5.8, p=0.009), and no difference in waketime. Breast cancer was associated with a bedtime that was 41.1 minutes later (95%CI 10.3,72.0, p=0.009) and a waketime that was 48.7 minutes later (95%CI 12.5,84.9, p=0.008), but no difference in sleep duration. No statistically significant effects were seen for either type of skin cancer, melanoma or non-melanoma.

Conclusion

Prostate cancer was associated with an earlier bedtime and associated increased sleep time. Breast cancer, on the other hand, was associated with a phase delay of the sleep period but no change in sleep duration. Skin cancer was not associated with differences in sleep duration or timing. These findings may have implications for not only treatment of sleep problems in different types of cancer, but also possible circadian mechanisms.

Support

Dr. Grandner is supported by R01MD011600

0865 Menstrual Regularity And Bleeding Associated With Sleep Duration, Sleep Quality, And Daytime Sleepiness In A Community Sample

Introduction

Female menstrual health and its relationship to sleep duration, quality, fatigue and other factors is an understudied subject in the field of sleep and women’s health. Few studies examined sleep in relation to menstrual regularity and bleeding.

Methods

Data were obtained from N=579 women who have had a menstrual period in the past 12 months who participated in the Sleep and Health Activity, Diet, Environment, and Socialization (SHADES) study, a community-based sample of adults age 22-60 living in southeastern Pennsylvania. Participants were asked, “How regular is your period?” with response choices of “Very Regular,” “Mostly Regular,” “Fairly Regular,” and “Not Regular.” They were also asked, “How much bleeding do you usually experience during your period?” Responses were “Very Heavy,” “Medium,” “Light,” or “Very Light.” These were evaluated as ordinal outcomes. Sleep-related predictors included sleep duration (<=6h [Short], 7-8 [Normal], and >=9 [Long]), Insomnia Severity Index (ISI) score, Pittsburgh Sleep Quality Index (PSQI) score, Epworth Sleepiness Scale (ESS) score, and Fatigue Severity Scale (FSS) score. Covariates included age, education, income, race/ethnicity, and body mass index.

Results

Short sleep duration was associated with a greater likelihood of heavier bleeding (OR=1.46, p=0.026) and greater irregularity (OR=1.44, p=0.031), compared to Normal sleep. Higher PSQI score was associated with more irregularity (OR=1.05, p=0.022). FSS score was associated with both heavier bleeding (OR=1.02, p=0.003) and more irregularity (OR=1.02, p=0.008). Long sleep, ISI, and ESS were not associated with either outcome. A sleep duration by FSS score interaction was found for irregularity (p=0.1). Among Normal sleepers, FSS was associated with greater irregularity, but not among Short sleepers.

Conclusion

There is a relationship between short sleep and heavier and irregular menses. These findings have implications for treating sleep problems among women. Also, mechanisms of these associations should be explored further.

Support

Dr. Grandner is supported by R01MD011600

The SHADES study was funded by R21ES022931

0544 Reconsidering Stimulus Control: Activities in Bed Associated with Sleep-Related Outcomes

Introduction

The typical advice is that in order to avoid insomnia, people should avoid activities in bed other than sleep. Yet, activities such as reading and watching TV in bed are common.

Methods

Data were obtained from the Sleep and Health Activity, Diet, Environment, and Socialization (SHADES) Study, N=1,007 adults age 22-60. Sleep hygiene was assessed using items from the Sleep Practices and Attitudes Questionnaire (SPAQ), which asked whether respondents agree/disagree that they do the following in bed: Read, Watch TV, Eat, Work, Worry, and/or Argue. These were analyzed in relation to Insomnia Severity Index (ISI) score, Pittsburgh Sleep Quality Index (PSQI) score, Epworth Sleepiness Scale (ESS) score, Fatigue Severity Scale (FSS) score, and self-reported sleep duration (TST), sleep latency (SL), and wake after sleep onset (WASO). Covariates included age, sex, education, and income.

Results

Those that frequently engaged in activities were: reading (75%), watching TV (63%), eating (42%), working (32%), worrying (82%), and arguing (23%). Reading was associated with less WASO (B=-14min, p=0.02). Watching TV was associated with higher ISI (B=1.22, p=0.04), PSQI (B=1.04, p=0.007), and ESS (B=0.87, p=0.049), and less TST (B=-0.29, p=0.04). Eating was associated with higher ISI (B=1.75, p=0.01), PSQI (B=1.23, p=0.008), and FSS (B=4.36, p=0.002). Working was associated with higher ISI (B=1.82, p=0.019), PSQI (B=1.65, p=0.001), and ESS (B=1.78, p=0.002). Worrying was associated with higher ISI (B=7.34, p<0.0005), PSQI (B=4.40, p<0.0005), ESS (B=2.53, p=0.001), FSS (B=9.51, p<0.0005), and SL (B=19.39, p<0.0005), and less TST (B=-0.55, p=0.023). Arguing was associated with higher ISI (B=3.78, p<0.0005), PSQI (B=3.15, p<0.0005), ESS (1.47, p=0.023), and SL (B=10.97, p=0.013), and lower TST (B=-0.71, p=0.001).

Conclusion

Individuals who perform mentally distressing activities such as worrying and arguing experience especially worse sleep, and those who read in bed have fewer awakenings.

Support

The SHADES study was funded by R21ES022931. Dr. Grandner is supported by R01MD011600.

1095 Use Of Mobile Devices At Night Associated With Mental Health In Young Adults

Introduction

Mobile technology use in bed is becoming commonplace and associated with habitual short sleep duration. The present study examined whether device use at night was related to mental health.

Methods

Data from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study comes from a community-based sample, which was restricted to N=473 between the ages of 22-29. Device use was assessed as presence in the room at night, any use at night, texting, emailing, browsing the internet, making or receiving calls, and using social media. Participants were also asked how often they are woken by a call/alert from their phone (unplanned), how often they are woken by their phone alarm (planned), and how often they check their phone at night. These were recorded as never, rarely, some nights, almost every night, and every night, and were assessed as an ordinal outcome. Predictors included score on the Patient Health Questionnaire depression scale (PHQ9), GAD7 anxiety scale, Perceived Stress Scale (PSS), and Multidimensional Scale of Perceived Social Support (MSPSS). Ordinal logistic regression analyses were adjusted for age, sex, race/ethnicity, education, and income.

Results

Depression was associated with texting (oOR=1.03, p=0.025), email (oOR=1.03, p=0.022), internet (oOR=1.05, p=0.003), unplanned awakenings (oOR=1.05, p=0.001), and checking the phone (oOR=1.09, p<0.0005). Anxiety was associated with texting (oOR=1.05, p=0.001), email (oOR=1.05, p=0.001), internet (oOR=1.05, p=0.002), social media (oOR=1.04, p=0.009), unplanned awakenings (oOR=1.06, p<0.0005), planned awakenings (oOR=1.04, p=0.025), and checking the phone (oOR=1.10, p<0.0005). Perceived stress was associated with internet (oOR=1.02, p=0.034), unplanned awakenings (oOR=1.02, p=0.045), and checking (oOR=1.04, p<0.0005). Social support was associated with decreased checking (oOR=0.98, p=0.018).

Conclusion

Mobile device use at night itself is not associated with mental health, but specific activities may be. Also, those who report more disruptions from the device and more checking of the device also report worse mental health. Relationships might be bidirectional.

Support

Dr. Grandner is supported by R01MD011600

The SHADES study was funded by R21ES022931

0232 Impact of Mental Health on 10-Year Trends in Habitual Sleep Duration

Introduction

Public health efforts aimed at reducing the decline in habitual sleep duration have not been successful. It is possible that this decline is differentially experienced relative to individuals’ mental health status. This would further support the need to focus on mental health as a strategy for improving sleep in the general population.

Methods

We examined 10 years of the National Health Interview Survey data (N=305,555). During all years, habitual sleep duration, age, sex, race/ethnicity, and height and weight (used to compute body mass index) were recorded in the same way. In addition, depressed mood in the past 30 days was evaluated (coded as none, mild, moderate, or severe). Weighted regression analyses examined sleep duration as an outcome, year and depressed mood as predictors, and sociodemographics as covariates. A year-by-depressed mood interaction was computed, and analyses were stratified by group.

Results

There was a significant year-by-depression interaction on linear change in sleep duration over the 10 year period (p=0.0001). Analyses were then stratified by depressed mood. In adjusted analyses, individuals with no depressed mood lost an average of 0.68 minutes of sleep per year (95%CI -0.82,-0.55; p<0.0001). Among those with mild depression, this was 7% higher, at 0.73 minutes (95%CI -1.13,-0.33; p<0.0001). Among those with moderate depressed mood, this was 154% higher, at 1.73 minutes lost per year (95%CI -2.31,-1.16; p<0.0001). Among those with severe depressed mood, this was 351% higher, at 3.07 minutes per year (95%CI -4.22,-1.92; p<0.0001).

Conclusion

The 10-year linear decline in habitual sleep duration seems to depend on mental health status. Individuals with better mental health lose less sleep over time, relative to those with worse mental health. This highlights the importance of mental health as a possible avenue for improving sleep health in the population.

Support

Dr. Grandner is supported by R01MD011600

0226 Sleep Duration and Symptoms Associated with Race/Ethnicity in Elite Collegiate Athletes

Introduction

Previous studies have documented sleep disparities in the general population. Given the increased interest in sleep among athletes, and the degree to which demographics and schedules among athletes differ from the general population, this analysis aims to examine the relationship between race/ethnicity and sleep duration and symptoms among elite college athletes.

Methods

Data were obtained from N=189 Division-1 collegiate athletes across a wide range of sports played. Race/ethnicity was self-reported and categorized as Non-Hispanic White, Black/African-American, Hispanic/Latino, Asian, and American Indian/Alaskan Native. Outcomes of interest included self-reported typical sleep duration (in hours), CESD depression score, and frequency of sleep symptoms, assessed using items from the Sleep Disorders Symptom Check List (difficulty falling asleep, difficulty staying asleep, early morning awakenings, tiredness, sleepiness, loud snoring, choking/gasping, fragmentation, hypnogogic/pompic hallucinations, sleep paralysis, and nightmares). Sleep duration and depression were evaluated with linear regression, and symptoms were evaluated as ordinal. Covariates included age and sex.

Results

Compared to Non-Hispanic Whites, Blacks/African-Americans reported less sleep (B=-0.80, p<0.0005), more depression (B=2.85, p=0.046), more difficulty maintaining sleep (oOR=2.12, p=0.034), early morning awakenings (oOR=3.15, p=0.001), and sleepiness (oOR=2.11, p=0.048); Hispanic/Latinos reported more hypnogogic/pompic hallucinations (oOR=2.90, p=0.007), sleep paralysis (oOR=2.72, p=0.026), and nightmares (oOR=2.22, p=0.035); Asians reported more depression (B=4.46, p=0.028), sleepiness (oOR=5.06, p=0.003), loud snoring (oOR=4.71, p=0.018), and sleep paralysis (oOR=3.57, p=0.031); and American Indians/Alaskan Natives reported less sleep (B=-1.00, p=0.018).

Conclusion

Racial/ethnic differences in sleep duration and sleep symptoms were seen among athletes. Future studies will be needed to replicate and further explain these findings.

Support

The REST study was funded by an NCAA Innovations grant. Dr. Grandner is supported by R01MD011600

0240 College Football Players Compared to Other Collegiate Athletes: Symptoms of Insufficient Sleep Duration, Insomnia, and Sleep Apnea

Introduction

College athletes experience frequent sleep disturbances. Data from professional football players suggests high rates of sleep apnea symptoms. Little data is available on college football players, especially compared to other athletes.

Methods

Data were obtained from N=189 NCAA Division-I student athletes, including N=45 football players). Outcomes of interest came from the Sleep Disorders Symptom Check List (SDSCL) which rated symptoms on a frequency scale of 0: never and 4: >5 times/week. Symptoms evaluated were daytime tiredness, any snoring, loud snoring, breathing pauses during sleep, and waking up choking/gasping sleep apnea), as well as difficulty falling asleep, difficulty with nighttime awakenings, and early morning awakenings (insomnia). Other outcomes include self-reported sleep duration, Insomnia Severity Index, frequency of caffeine use, and frequency of use of medications to help with sleep. Linear and ordinal logistic regression analyses were adjusted for age, sex, year in school, socioeconomic status, and mood. Post-hoc analyses examined men only.

Results

Regarding sleep apnea symptoms, football players reported more snoring (oOR=3.14, p=0.01), loud snoring (oOR=4.38, p=0.008), breathing pauses (oOR=5.42, p=0.0499), and choking/gasping (oOR=8.51), but not daytime tiredness. Regarding insufficient sleep, football players reported no difference in sleep duration but decreased caffeine use (oOR=0.27, p=0.002). Regarding insomnia, football players showed no difference in ISI scores or insomnia symptoms, but increased likelihood of sleeping pill use (oOR=3.01, p=0.03). When analyses were restricted to men only, all of these relationships were maintained.

Conclusion

College football athletes may exhibit different sleep symptoms than other college athletes, as they exhibit more sleep apnea-related symptoms, without the increase in daytime symptoms, such as tiredness.

Support

The REST study was funded by an NCAA Innovations grant.

Dr. Grandner is supported by R01MD011600

0375 Culturally Consistent Diet Among Individuals of Mexican Descent at the Us-Mexico Border is Associated with Sleep Duration and Quality

Introduction

Previous studies have shown that people who consume culturally consistent foods have improved cardiometabolic profiles. Few studies have examined whether this finding extends to sleep health.

Methods

Data were collected from N=100 adults (age 18-60, 53% female) of Mexican descent in the city of Nogales, AZ (66% not born in the US, 33% 1st-generation). Surveys were presented in English or Spanish. Acculturation was assessed with the Acculturation Scale for Mexican-Americans (ARSMA-II), which has separate scales for Mexican and Anglo acculturation (subscale range 0-4). A supplemental ARSMA item asks how often “My family cooks Mexican foods.” Responses were coded as either frequent or infrequent. Insomnia was assessed with the Insomnia Severity Index (ISI), Sleepiness with the Epworth Sleepiness Scale (ESS), Sleep quality with the Pittsburgh Sleep Quality Index (PSQI), and Sleep duration and sleep medication use with PSQI items. Regression analyses examined these outcomes relative to whether individuals frequently consumed Mexican foods. Covariates included age, sex, and acculturation scores. Parental education level was also included, as an indicator of childhood socioeconomic status and since food culture likely involves parents.

Results

Regular consumption of Mexican foods was associated with 1.41 more hours of sleep, on average (95%CI 0.19,2.62, p<0.05). It was also associated with a decreased likelihood of snoring (oOR=0.25; 95%CI 0.07,0.93; p<0.05). No differences were seen for PSQI, ISI, or ESS score.

Conclusion

Individuals of Mexican descent at the US-Mexico border who regularly consume culturally consistent food report overall more sleep and less snoring. Previous studies show that Mexican acculturation may be associated with improved sleep sufficiency; it is possible that this reflects an overall healthier lifestyle that also includes a culturally consistent diet. Further studies would be beneficial to help determine the role acculturation plays in sleep and diet and how it effects cardiometabolic risk.

Support

Dr. Grandner is supported by R01MD011600. This work was supported by a UAHS grant.

1093 Population-level Suicide Ideation: Impact Of Combined Roles Of Sleep Duration, Sleep Disturbance, And Daytime Sleepiness

Introduction

Poor sleep has been shown to be related to suicide ideation and depressed mood, but population-level studies have not been done to explore the specific issues within sleep that effect mood, specifically leading to suicide ideation.

Methods

Data from adults 18 and older in the 2015-2016 National Health and Nutrition Examination Survey (NHANES) who provided complete data were used (N=5,123). Suicide ideation was recorded as the presence of thinking that “you would be better off dead” in the past 2 weeks. Sleep duration was recorded in half-hour increments and transformed to represent absolute distance from 7 hours (to model u-shaped association). Sleep disturbance was recorded as presence of “difficulty falling asleep, staying asleep, or sleeping too much” non, several days, or more than half the days of the past 2 weeks. Sleepiness was frequency feeling “overly sleepy during the day” in the past 12 months. Covariates included age, sex, race/ethnicity, and presence of depressed mood in the past 2 weeks. Additional impact of difficulty thinking/concentrating in the past 2 weeks was explored. NHANES sample weights were used in analyses.

Results

In adjusted analyses, increase likelihood of suicide ideation was associated with distance from 7hrs (OR=1.24/hr, p=0.008), sleep difficulties most of the time (OR=2.46, p=0.001), but not sleepiness. When both sleep variables were adjusted for each other, results remained significant for U-shaped sleep duration (OR=1.21/hr, p=0.02) and sleep disturbance (OR=2.31, p=0.003). These were attenuated but remained significant when difficulty thinking/concentrating was introduced; a significant sobel test (p<0.0001) suggested partial mediation, with this variable accounting for approximately 13% of the variance of the relationship to sleep.

Conclusion

In the population, improper and poor sleep was associated with a greater risk of suicide ideation.

Support

Dr. Grandner is supported by R01MD011600

0140 Dietary Macronutrients and Sleep Duration, Sleep Disturbance, and Daytime Fatigue

Introduction

We examined nationally-representative data on macronutrients associated with multiple dimensions of sleep health.

Methods

Data were obtained from the 2015–2016 National Health and Nutrition Examination Survey, (N=5,266 adults). Standard 24-h dietary recall procedures were analyzed to establish daily consumption of protein, carbohydrates, sugar, fiber, total fat, and saturated fat. Self-reported habitual sleep duration was categorized as very short (<5h), short (5–6.5h), normal (7-8h), and long (>8h). Sleep disturbance and daytime tiredness/fatigue were self-reported as either none, mild, moderate, or severe. Weighted multinomial logistic regressions with sleep variables as outcome/dependent variable and percent of each macronutrient as independent variable were adjusted for age, sex, race/ethnicity, education, and body mass index.

Results

Increased protein was associated with a decreased likelihood of very short sleep (RRR=0.01, p=0.019) and severe fatigue (RRR=0.06, p=0.020). Increased carbohydrates was associated with an increased likelihood of very short (RRR=61.17, p=0.001), short (RRR=3.96, p=0.017), and long (RRR=2.58, p=0.041) sleep, severe sleep disturbance (RRR=9.37, p=0.010) and fatigue (RRR=7.61, p=0.009). Increased sugar was associated with an increased likelihood of very short (RRR=24.17, p=0.001), short (RRR=3.29, p=0.017), and long (RRR=2.22, p=0.046) sleep, as well as mild (RRR=2.36, p=0.041) and severe (RRR=10.70, p=0.001) sleep disturbance, and severe fatigue (RRR=12.98, p<0.0005). Increased fiber was associated with a decreased likelihood of long (RRR=0.01, p=0.032) sleep and severe sleep disturbance (RRR<0.01, p<0.0005), as well as moderate (RRR<0.01, p=0.026) and severe (RRR<0.01, p<0.0005) fatigue. Increased fat was associated with a decreased likelihood of very short sleep (RRR=0.01, p=0.010). Increased saturated fat was associated with a decreased likelihood of very short sleep (RRR<0.01, p=0.017).

Conclusion

Protein and fiber were associated with better sleep profiles overall and carbohydrate and sugar were associated with worse sleep, as well as increased prevalence of sleep disturbances and fatigue.

Support

Dr. Grandner is supported by R01MD011600

0236 Team-Based Athletes Sleep Less Than Individual Athletes, But Do Not Report More Insomnia or Fatigue

Introduction

Collegiate student-athletes face challenges balancing academics and athletics, and getting an adequate amount of sleep is one factor that can assist in sustaining an elite level of play. Team-based sports may present with systematically different sets of demands.

Methods

Data were obtained at the start of the academic semester from N=189 NCAA Division-1 athletes from a wide range of sports. The sample was 46% female. Individuals were classified as playing in a team sport (e.g., football, basketball, baseball, softball, volleyball) or an individual sport (e.g., swimming, track, golf). Sleep-related outcomes included self-reported sleep duration and sleep latency, frequency of sleeping pill use (Never, Rarely, Sometimes, Often), Insomnia Severity Index score, and Fatigue Severity Scale score. Regression analyses were adjusted for age and sex.

Results

In adjusted analyses, team-based athletes reported 22.4 minutes less sleep than individual athletes (95%CI -42.8,-1.9; p<0.05). They also reported 5.6 less minutes of sleep latency (95%CI -10.8,-0.3; p<0.05). More frequent sleeping pill use was also reported (oOR=0.96; 95%CI: 0.26,1.67; p=0.007). They did not report any differences in insomnia or daytime fatigue levels.

Conclusion

These results suggest that even though team-based athletes may not report more sleep complaints or daytime complaints, they may be at increased risk for less sleep and more sleep medication. Further work is needed to identify the sources of these differences to guide interventions.

Support

The REST study was funded by an NCAA Innovations grant.

Dr. Grandner is supported by R01MD011600

0235 Sleep Duration and Timing Associated with Eating Behaviors: Data from NHANES 2015–2016

Introduction

Previous studies have shown that, in the laboratory, sleep deprivation leads to unhealthy eating patterns. In real-world samples, lack of sleep is associated with obesity. Few real-world studies of sleep and food intake patterns exist, especially from nationally-representative samples.

Methods

Data from the 2015–2016 National Health and Nutrition Examination Survey (NHANES) were used. NHANES is a national-representative survey collected by the CDC. N=6,291 participants provided data about dietary behaviors and sleep timing. Dietary behaviors included the number of meals not made at home in the past 7 days (NOTHOME), number of fast food/pizza meals in the past 7 days (FASTFOOD), number of pre-made meals in the past 30 days (PREMADE), and number of frozen meals in the past 30 days (FROZEN). Linear regression models examined these as outcomes and predictors including bedtime (minutes), waketime (minutes), sleep duration (hours), and daytime tiredness/fatigue (never, rarely, sometimes, often). Covariates included age, sex, education, income/poverty ratio, race/ethnicity, and body mass index.

Results

Number of meals not made at home (NOTHOME) was associated with a later bedtime (B=2.25, p=0.01) and shorter sleep duration (B=-0.12, p=0.01). FASTFOOD was associated with shorter sleep (B=-0.13,p=0.003) and tiredness/sleepiness sometimes (B=0.77, p=0.007) and often (B=0.55, p=0.03). FROZEN meals were associated with a later waketime (B=3.31, p=0.003) and tiredness/sleepiness sometime (B=1.20, p=0.025) and often (B=1.60, p=0.04). A sleep duration by bedtime interaction was not significant for any outcomes. In models that included overall levels of anxiety, these relationships were maintained.

Conclusion

This is one of the largest studies to show that habitual sleep patterns are associated with real-world food choices. In particular, shorter sleep duration and tiredness/sleepiness are associated with more ready-made and fast food meals. It is possible that lack of sleep leads to worse food choices, or that stress leads to both lack of sleep and easier food options. Given the often poor nutritive value of foods consumed outside the home and pre-prepared foods, these associations may in part explain the influence of sleep on cardiometabolic risk factors.

Support

Dr. Grandner is supported by R01MD011600

0372 Disparities in Sleep Timing in the US: Data From the National Health and Nutrition Examination Survey 2015-2016

Introduction

Several studies have demonstrated population-level disparities in sleep duration and sleep quality. Population-level estimates of bedtime and waketime have been unavailable. Considering the important role of circadian rhythms in health, population-level disparities in timing have important public health implications.

Methods

Data from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) from the CDC were used (N=4,491). Typical time in and out of bed were assessed and were converted to minutes. Race/ethnicity was self-reported and coded as non-Hispanic White, Black/African-American, Mexican-American, Other Hispanic/Latino, Asian, and Multiracial/Other. Covariates included age, sex, education level, income/poverty ratio, body mass index, and overall health. Additional models controlled for habitual sleep duration, frequency of sleep disturbance, depressed mood, and daytime tiredness/fatigue. Multiple linear regression analyses with time as an outcome were weighted using CDC-provided NHANES sample weights.

Results

In adjusted analyses, compared to non-Hispanic Whites, Blacks/African-Americans went to bed 29.4 mins later (p<0.0005), Asians went to bed 37.0 mins later (p<0.0005) and woke 27.7 mins later (p<0.0005), and Mexican-Americans woke 16.3 mins earlier (p=0.018). After further adjustment for sleep duration and sleep disturbances, Blacks/African-Americans went to bed 22.1 mins later (p<0.0005) and woke 22.2 mins later (p<0.0005), and Asians went to bed 36.1 mins later (p<0.0005) and woke 40.6 mins later (p<0.0005). These relationships remained generally unchanged when depressed mood and daytime tiredness/fatigue were adjusted in the model.

Conclusion

This is the first nationally-representative study to demonstrate population-level disparities in sleep timing. Specifically, Blacks/African-Americans and Asians present with delayed sleep, even after adjusting for other aspects of sleep.

Support

Dr. Grandner is supported by R01MD011600