Dimensions Of Sleep Health Research Articles

Sleep, emotional supportiveness, and socially straining behavior: A multidimensional approach

ObjectiveTo determine the associations of emotional supportiveness toward others and engagement in socially straining (negative) behavior toward others across close relationships with multiple dimensions of sleep health. DesignCross-sectional. SettingCommunity sample from the Midlife in the United States study (MIDUS). ParticipantsFour-hundred and thirty-five participants from the MIDUS II Biomarker Project aged 35–85. MeasurementsSelf-report assessments of being emotionally supportive and engaging in socially straining behavior toward friends, family, and romantic partners; self-report assessments of demographic and other psychological and health variables; 7 nights of wrist actigraphy and sleep diary. ResultsBeing emotionally supportive and engagement in socially straining behavior were associated with multiple dimensions of sleep health. The inclusion of demographic, health, and psychological covariates reduced but did not eliminate these associations. Based on analyses adjusting for these covariates, being more emotionally supportive toward close others was most robustly related to higher daytime alertness, and engaging in more socially straining behavior was most robustly related to less sleep regularity, quality, and efficiency. ConclusionsThese findings implicate sleep health as a substantive correlate of being emotionally supportive toward and imposing social strain on others. They show that both daytime and nighttime dimensions of sleep health are important for social functioning across close relationships and highlight the need to examine both positive and negative aspects of relationships in relation to sleep.

The influence of sleep health on dietary intake: a systematic review and meta-analysis of intervention studies.

Poor dietary intake increases disease risk, and poor sleep influences diet. This systematic review and meta-analysis of intervention studies aimed to evaluate the effect of sleep health on dietary intake in adults. Five online databases were used to identify studies published between 1970 and 2019. Included studies were interventions that modified sleep and reported dietary outcomes. Fifty four full texts were assessed and 24 publications were included. Following risk of bias appraisal, data were narratively summarised and a sub-group of studies (n=15) was meta-analysed to determine the effect of sleep on dietary intake. One intervention modified sleep timing and 23 modified duration. Sleep duration was partially restricted (≤5.5hnight-1 ) (n=16), totally restricted (n=4), partially and totally restricted (n=1), and extended (n=2). Dietary outcomes were energy intake (n=24), carbohydrate, fat, protein intake (n=20), single nutrient intake (n=5), diet quality (n=1) and food types (n=1). Meta-analysis indicated partial sleep restriction results in higher energy intake in intervention compared with control [standardised mean difference (SMD)=0.37; 95% confidence interval (CI)=0.21-0.52; P<0.001], with a mean difference of 204kcal (95% CI=112-295; P<0.001) in daily energy intake, and a higher percentage of energy from fat, protein, carbohydrate (fat: SMD=0.33; 95% CI=0.16-0.51; P<0.001; protein: SMD=0.30, 95% CI=0.12-0.47, P=0.001; carbohydrate: SMD=0.22, 95% CI=0.04-0.39, P=0.014). Partial sleep restriction with duration of ≤5.5hday-1 increases daily energy intake, as well as fat, protein and carbohydrate intake. Further research is needed to determine the relationship between other dimensions of sleep health and dietary intake.

0366 Sleep Health Dimensions, Disturbances, and Disruptors Among White, Black, Hispanic/ Latina, and Asian Women

Abstract Introduction Despite the importance of sleep for health promotion and disease prevention, data are limited regarding the distribution of multiple sleep health dimensions, disturbances, and disruptors among women, especially racial/ethnic minorities who disproportionately experience poor sleep. Methods To determine the prevalence of sleep health, disturbances, and disruptors (e.g., short sleep duration, sleep debt, insomnia symptoms, light exposure at night) overall and among Black, Hispanic/Latina, and Asian compared to White women, we used cross-sectional data collected by the Sister Study at enrollment (2003-2009) and two follow-ups (2012-2014, 2014-2016). Adjusting for sociodemographics, health behaviors, and health conditions including depression, we used Poisson regression with robust variance to estimate prevalence ratios (PRs) for unfavorable sleep among racial/ethnic minority compared to White women. Results Of the 49,874 eligible women (mean age ± standard deviation: 55.7 ± 9.0 years, 84.8% ≥high school education, 74.7% married) 85.3% were White, 9.0% Black, 5.1% Hispanic/Latina, and 0.7% Asian. Overall, 70% reported the recommended amount of sleep, 15.7% inconsistent weekly sleep patterns, 26% sleep debt, and 14% insomnia symptoms plus short sleep. Racial/ethnic minorities were much more likely than whites to report very short (≤5 hours) sleep (PRBlack)=5.98[95% Confidence Interval: 4.67-7.66]; PRLatina=2.83[1.98-4.04]; PRAsian=5.41[2.41-12.13] and to report needing &amp;lt;7 hours to feel their best (PRBlack=2.95[2.75-3.17]; PRLatina=1.85[1.65-2.07]; PRAsian=2.66[2.10-3.37]). Black and Hispanic/Latina women had a higher prevalence than whites of insomnia, short sleep plus insomnia, inconsistent sleep, sleep debt, and frequent napping; however, all racial/ethnic minorities were less likely to report daytime sleepiness (PRBlack=0.82[0.78-0.85]; PRLatina=0.94[0.89-0.98]; PRAsian=0.79[0.69-0.92]) and restless leg syndrome. Witnessed sleep apnea was higher among Black women, and REM sleep disorder did not differ across racial/ethnic groups. Sleeping with room lights or a television on was more prevalent among racial/ethnic minorities (PRBlack=1.78[1.71-1.86]; PRLatina=1.27[1.17-1.37]; PRAsian=1.62[1.32-1.99]). Conclusion Poor sleep health, disturbances, and disruptors were prevalent among women and varied across racial/ethnic groups in ways that may contribute to health disparities. Support This work was funded by the Intramural Program at the National Institutes of Health, National Institute of Environmental Health Sciences (Z1A ES103325-01 to (CLJ) and Z01 ES044005 to (DPS)).

0854 Exploration Of Sleep Health Among Middle-aged And Older Adults

Abstract Introduction Sleep health, a construct introduced to characterize the multidimensional attributes of sleep, has been explored in a variety of populations; however has not been adequately examined for middle-aged and older adults. As attributes of sleep may change with age, the dimensional structure of sleep health may differ in this population. This study aimed to validate a composite measure of sleep health among middle-aged and older adults using data from the Health and Retirement Survey (HRS). Methods Data from the 2014 Core survey of the HRS was used to create a composite measure of Sleep Health including sleep efficiency, duration, timing, satisfaction, and alertness. We standardized and averaged the original variables before transforming to T scores. Sleep Health T scores (ranging 0-100, higher scores indicating better sleep health) were examined using exploratory and confirmatory factor analysis (EFA; CFA). Results Our sample included 6,095 adults with mean age of 68 years (SD=10.1; range 50-99 years). The majority were female (59.7%), white (77%), with high school education (53.9%). Sleep Health T scores ranged from 27-61 (mean=50; SD=6.7). EFA identified one factor. Timing was removed due to low factor loading (&amp;lt;0.4). The revised four-dimension composite Sleep Health measure had acceptable reliability (Cronbach’s alpha 0.6). CFA showed a well-adjusted model (REMSA=0.097; NFI=0.964; RMR=0.035; GFI=0.990; AGFI=0.951). Conclusion These results suggest that the composite measure was valid for assessing sleep health among middle-aged and older adults. Limitations include the use of secondary data, as sleep health dimensions were based on variables not created specifically for our research question. Future research should further examine the role of sleep timing in overall sleep health among middle-aged and older adults. Support This study was supported by the University at Buffalo Clinical and Translational Science Institute (CTSI) funded by the National Institutes of Health (Lorenz, PI).

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

Abstract 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 (&amp;lt;5h), short (5–6.5h), normal (7-8h), and long (&amp;gt;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&amp;lt;0.0005). Increased fiber was associated with a decreased likelihood of long (RRR=0.01, p=0.032) sleep and severe sleep disturbance (RRR&amp;lt;0.01, p&amp;lt;0.0005), as well as moderate (RRR&amp;lt;0.01, p=0.026) and severe (RRR&amp;lt;0.01, p&amp;lt;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&amp;lt;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

A workshop report on the causes and consequences of sleep health disparities.

Sleep deficiencies, which include insufficient or long sleep duration, poor sleep quality, and irregular timing of sleep, are disproportionately distributed among populations that experience health disparities in the United States. Sleep deficiencies are associated with a wide range of suboptimal health outcomes, high-risk health behaviors, and poorer overall functioning and well-being. This report focuses on sleep health disparities (SHDs), which is a term defined as differences in one or more dimensions of sleep health on a consistent basis that adversely affect designated disadvantaged populations. SHDs appear to share many of the same determinants and causal pathways observed for health outcomes with well-known disparities. There also appears to be common behavioral and biological mechanisms that connect sleep with poorer health outcomes, suggesting a link between SHDs and other health disparities observed within these designated populations. In 2018, the National Institute on Minority Health and Health Disparities, the National Heart, Lung, and Blood Institute, and the Office of Behavioral and Social Sciences Research convened a workshop with experts in sleep, circadian rhythms, and health disparities to identify research gaps, challenges, and opportunities to better understand and advance research to address SHDs. The major strategy to address SHDs is to promote integration between health disparity causal pathways and sleep and circadian-related mechanisms in research approaches and study designs. Additional strategies include developing a comprehensive, integrative conceptual model, building transdisciplinary training and research infrastructure, and designing as well as testing multilevel, multifactorial interventions to address SHDs.

Racial/Ethnic Disparities in Sleep Health and Potential Interventions Among Women in the United States.

While essential for health and wellness, the various dimensions of sleep health are generally not equitably distributed across the population, and reasons for racial/ethnic sleep disparities are not fully understood. In this review, we describe racial/ethnic sleep disparities and subsequent implications for health from prior and recently conducted epidemiological and clinical studies as well as the potential sleep interventions presented at the 2018 Research Conference on Sleep and the Health of Women at the National Institutes of Health. Given the clear connection between sleep and poor health outcomes such as cardiovascular disease, we concluded that future studies are needed to focus on sleep health in general, sleep disorders such as insomnia and obstructive sleep apnea in particular, and disparities in both sleep health and sleep disorders among women using an intersectional framework. Future research should also integrate sleep into interventional research focused on women's health as these results could address health disparities by informing, for example, future mobile health (mHealth) interventions prioritizing women beyond the clinical setting.

A composite measure of sleep health is associated with symptoms of depression among Japanese female hospital nurses

BackgroundIndividual dimensions of sleep health, including satisfaction, sleepiness/alertness, timing, efficiency, and duration, are associated with depression. We investigated whether a composite sleep health score is associated with symptoms of depression among Japanese female hospital nurses. MethodsParticipants were nurses (n = 2482, all women, age 31.2 ± 8.9 years) working at three general hospitals in Tokyo, Japan. A cross-sectional survey, conducted in 2015, assessed self-reported sleep and symptoms of depression. Sleep health was categorized as “good” or “poor” across five dimensions: satisfaction, daytime sleepiness, mid-sleep time, efficiency, and duration. A composite sleep health score was calculated by summing the number of “poor” dimensions. Depression was defined by depressed mood, loss of interest, or at least one of those symptoms (“depression symptoms”). Associations between sleep health and symptoms of depression were evaluated with multivariate logistic regression analyses, adjusting for sociodemographic factors and hypnotic medication use. ResultsIn multivariate logistic regression analyses, sleep health symptoms of poor satisfaction, efficiency, and duration were significantly associated with depressed mood; daytime sleepiness and poor efficiency were significantly associated with loss of interest; and poor satisfaction, daytime sleepiness, mid-sleep time, and efficiency were significantly associated with having at least one depressive symptom. The composite sleep health score was associated in a graded fashion with greater odds of depression symptoms. ConclusionIndividual and composite sleep health scores were associated with symptoms of depression. Assessing composite measures of multidimensional sleep health may help to better understand the well-known associations between poor sleep and depression and lead to improved intervention strategies.

Multidimensional sleep health and subsequent health-care costs and utilization in older women.

Determine the association of poor multidimensional sleep health with health-care costs and utilization. We linked 1,459 community-dwelling women (mean age 83.6 years) participating in the Study of Osteoporotic Fractures Year 16 visit (2002-2004) with their Medicare claims. Five dimensions of sleep health (satisfaction, daytime sleepiness, timing, latency, and duration) were assessed by self-report. The number of impaired dimensions was expressed as a score (range 0-5). Total direct health-care costs and utilization were ascertained during the subsequent 36 months. Mean (SD) total health-care costs/year (2017 dollars) increased in a graded manner across the sleep health score ranging from $10,745 ($15,795) among women with no impairment to up to $15,332 ($22,810) in women with impairment in three to five dimensions (p = 0.01). After adjustment for age, race, and enrollment site, women with impairment in three to five dimensions vs. no impairment had greater mean total costs (cost ratio [CR] 1.34 [95% CI = 1.13 to 1.60]) and appeared to be at higher risk of hospitalization (odds ratio (OR) 1.31 [95% CI = 0.96 to 1.81]). After further accounting for number of medical conditions, functional limitations, and depressive symptoms, impairment in three to five sleep health dimensions was not associated with total costs (CR 1.02 [95% CI = 0.86 to 1.22]) or hospitalization (OR 0.91 [95% CI = 0.65 to 1.28]). Poor multidimensional sleep health was not related to outpatient costs or risk of skilled nursing facility stay. Older women with poor sleep health have higher subsequent total health-care costs largely attributable to their greater burden of medical conditions, functional limitations, and depressive symptoms.

Association between sexually explicit messages and sleep health among French sexual minority men

It has been demonstrated that sexual minority men (SMM) participate in sexting. While research has shown that engagement in the exchange of sexually explicit media is associated with poor health outcomes, no previous research has investigated its association with sleep health outcomes. This study sought to examine the association between sexually explicit media and sleep health among SMM, a population that suffers from poor sleep health. A popular geosocial networking application was used to recruit SMM individuals (N = 580) in the Paris, France, metropolitan area. Multivariate analyses, adjusting for sociodemographics, were used to test the association between the frequency of sexually explicit messaging and three dimensions of sleep health: (1) sleep quality, (2) sleep duration, and (3) two aspects of sleep problems. In multivariate analyses, those who reported engaging in sexually explicit messaging more were more likely to report getting less than seven hours of sleep (aRR = 1.24; 95% CI = 1.08, 1.43) compared with those who reported engaging in sexually explicit messaging less. No significant associations were found between sexting and sleep quality or reporting sleep problems. Sexually explicit messaging was associated with shorter sleep duration. Intervention targeted at individuals who sext could potentially improve sleep health outcomes.

Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: results from the Midlife in the United States (MIDUS) study.

Emerging evidence supports a multidimensional perspective of sleep in the context of health. The sleep health model, and composite sleep health score, are increasingly used in research. However, specific cutoff values that differentiate "good" from "poor" sleep, have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for sleep health dimensions and examined the relationship between sleep health and cardiometabolic morbidity. Participants from two independent Biomarker Studies in the MIDUS II (N = 432, 39.8% male, age = 56.92 ± 11.45) and MIDUS Refresher (N = 268, 43.7% male, age = 51.68 ± 12.70) cohorts completed a 1-week study where sleep was assessed with daily diaries and wrist actigraphy. Self-reported physician diagnoses, medication use, and blood values were used to calculate total cardiometabolic morbidity. Receiver operating characteristic (ROC) curves were generated in the MIDUS II cohort for each sleep health dimension to determine cutoff values. Using derived cutoff values, logistic regression was used to examine the relationship between sleep health scores and cardiometabolic morbidity in the MIDUS Refresher cohort, controlling for traditional risk factors. Empirically derived sleep health cutoff values aligned reasonably well to cutoff values previously published in the sleep health literature and remained robust across physical and mental health outcomes. Better sleep health was significantly associated with a lower odds of cardiometabolic morbidity (OR [95% CI] = 0.901 [0.814-0.997], p = .044). These results contribute to the ongoing development of the sleep health model and add to the emerging research supporting a multidimensional perspective of sleep and health.

Examination of sleep health dimensions and their associations with perceived stress and health in a UK sample.

Sleep health is a relatively new multidimensional concept, however, there is no consensus on its underlying dimensions. A previous study examined potential indicators of sleep health using an aggregated sleep health measure. However, the psychometric properties are yet to be determined. The primary aim was to assess the factor structure, reliability and validity of this measure. A secondary aim was to explore the relationships with perceived stress, and physical and mental health. A cross-sectional online survey was conducted with 257 adults from the UK aged 18-65 (78.4% female, mean age = 29.39 [SD = 11.37]). Participants completed 13 Sleep health items, the Pittsburgh Sleep Quality Inventory, Insomnia Severity Scale, Epworth Sleepiness Scale, Perceived Stress Scale and SF-12 Health Survey. The measure exhibited good internal consistency (α = 0.785) and construct validity as determined by associations with existing measures. Principle components analysis produced four factors e; sleep quality (α = 0.818), sleep adaptability (α = 0.917), sleep wellness (α = 0.621) and daytime functioning (α = 0.582). Adaptability (β = -241) was strongest predictor of perceived stress, and daytime functioning was strongest predictor of physical (β = 0.322) and mental health (β = 0.312). Sleep health is a multidimensional construct comprising four distinct but related dimensions. The importance of sleep health in terms of perceived stress and mental and physical health is highlighted.

Stress levels are associated with poor sleep health among sexual minority men in Paris, France

ObjectiveThe objective of this study was to examine the association between perceived stress and sleep health among a sample of sexual minority men (SMM). DesignCross-sectional survey. SettingParis, France. ParticipantsGay, bisexual and other SMM users ≥18 years on a geosocial networking application in Paris, France (N = 580). MeasurementsParticipants were directed to a web-based survey measuring stress, sleep health, and socio-demographics. Multivariate log-binomial regression models were used to estimate the adjusted risk ratios (aRRs) and 95% confidence intervals (CIs) to examine how stress may affect different dimensions of sleep health: 1) poor sleep quality, 2) short sleep duration, 3) problems falling asleep, and 4) problems staying awake in the daytime. ResultsMost participants (69.9%) reported at least sometimes feeling stressed (compared to never or rarely). Additionally, results demonstrate that higher perceived stress was associated with poorer sleep health; compared with those who reported feeling stress never or rarely, those who felt stress sometimes, often, or always were more likely to experience poor sleep quality (aRR = 6.67; 95% CI = 3.61–12.3), short sleep duration (aRR = 1.67; 95% CI = 1.17–2.38), problems falling asleep (aRR = 3.20; 95% CI = 2.26–4.52), and problems staying awake during the daytime (aRR = 3.52; 95% CI = 1.64–7.53). ConclusionElevated perceived stress can negatively influence sleep health among SMM in Paris, France.

Association of financial hardship with poor sleep health outcomes among men who have sex with men

Previous studies have identified an association between socioeconomic status and sleep health. While some research has studied this association among sexual minority groups, including men who have sex with men (MSM), they exclusively focused on US-based populations. The interplay between the two in shaping sleep health has not been previously examined on populations residing outside the US. This study considers both determinants, by investigating whether financial hardship is associated with sleep health among a sample of MSM in Paris, France. Broadcast advertisements were placed on a popular geosocial-networking smartphone application for MSM to direct users in Paris to a web-based survey measuring financial hardship and five dimensions of sleep health as well as socio-demographic characteristics. Modified Poisson models with robust error variance were computed to estimate risk ratios (RRs) and 95% confidence intervals (CI) for the associations between financial hardship and the following self-reported outcomes: 1) poor sleep quality, 2) short sleep duration; and 3) sleep problems. In total, 580 respondents completed the survey. In this sample, both financial hardship and poor sleep health were common - 45.5% reported that it was extremely, very, or somewhat difficult for them to meet their monthly payments on bills (referred to as “high financial hardship”) and 30.1% rated their sleep as fairly bad or very bad (referred to as “poor sleep quality”). Multivariate models revealed that, compared to participants who reported low financial hardship, those who reported high financial hardship were more likely to report poor sleep quality (aRR: 1.35, 95% CI: 1.04, 1.77), to report problems falling asleep (aRR: 1.23, 95% CI: 1.02, 1.49), and to report problems staying awake in the daytime (aRR: 3.12, 95% CI: 1.83, 5.31). Future research should investigate whether this relationship is causal and determine whether interventions to reduce financial hardships could promote sleep health among MSM.

0905 DEVELOPMENT OF THE PROMIS® SLEEP HEALTH MEASURES FOR CHILDREN AND ADOLESCENTS

There is no standard pediatric self-report measure of sleep health. Furthermore, existing measures have not been informed by stakeholders or developed using rigorous measurement science. The Patient Reported Outcomes Measurement Information System (PROMIS®) has produced over 100 person-centered measures that evaluate physical, mental, and social health in adults and children. Two PROMIS sleep-related measures (Sleep Disturbance and Sleep-Related Impairment) have been developed and psychometrically evaluated for adults. The purpose of this study was to develop and evaluate pediatric sleep health measures using PROMIS methodology. Concepts for self-report questions (items) were identified with input from sleep experts, clinicians, parents, youth, and systematic literature review. Items were iteratively revised based on cognitive interviews with 35 children and 21 parents, and then administered to 1104 children aged 8–17 and 1477 parents of children aged 5–17. Items were subjected to classic psychometric analysis and item response theory-based calibration. The initial item pool contained 112 items. Items were removed (52), revised (19), or added (1) based on cognitive interviews. Sixty-one items were field tested, with an additional 25 items removed based on psychometric properties. Two item banks measuring distinct sleep health dimensions were identified. Sleep Disturbance (SD, 12 items) captures difficulties with sleep initiation, sleep maintenance, and sleep quality. Sleep-Related Impairment (SRI, 13 items) assesses the impact of poor sleep on daytime functioning. SD and SRI item banks discriminate among children with a wide range of sleep health experiences. Items were selected for 8- and 4-item short forms to ensure adequate measurement precision across the full range of severity. An index of Sleep Practices (11 items) was also developed, with items focusing on sleep routines, technology use around sleep, and sleep timing. The PROMIS pediatric SD and SRI item banks are brief and psychometrically robust measures of patient-reported sleep experiences. These measures are meaningful and well-understood by children and parents, and provide efficient and precise measures of sleep in children across development, and in both general and clinical populations. The final measures are undergoing clinical validation in youth with sleep disorders, autism, asthma, and atopic dermatitis. PCORI SC-14-1403-12211.

An Aggregate Measure of Sleep Health Is Associated With Prevalent and Incident Clinically Significant Depression Symptoms Among Community-Dwelling Older Women.

Sleep can be characterized along multiple dimensions. We investigated whether an aggregate measure of sleep health was associated with prevalent and incident clinically significant depression symptoms in a cohort of older women. Participants were older women (mean age 80.1 years) who completed baseline (n = 6485) and follow-up (n = 3806) visits, approximately 6 years apart, in the Study of Osteoporotic Fractures (SOF). Self-reported sleep over the past 12 months was categorized as "good" or "poor" across 5 dimensions: satisfaction with sleep duration, daytime sleepiness, mid-sleep time, sleep onset latency, and sleep duration. An aggregate measure of sleep health was calculated by summing the number of "poor" dimensions. Clinically significant depression symptoms were defined as a score ≥6 on the Geriatric Depression Scale. Relationships between sleep health and depression symptoms were evaluated with multivariate logistic regression, adjusting for health measures and medications. Individual sleep health dimensions of sleep satisfaction, daytime sleepiness, mid-sleep time, and sleep onset latency were significantly associated with prevalent depression symptoms (odds ratios [OR] = 1.26-2.69). Sleep satisfaction, daytime sleepiness, and sleep onset latency were significantly associated with incident depression symptoms (OR = 1.32-1.79). The number of "poor" sleep health dimensions was associated in a gradient fashion with greater odds of prevalent (OR = 1.62-5.41) and incident (OR = 1.47-3.15) depression symptoms. An aggregate, multidimensional measure of sleep health was associated with both prevalent and incident clinically-significant depression symptoms in a gradient fashion. Future studies are warranted to extend these findings in different populations and with different health outcomes.