Study objectives

Short sleep duration and poor sleep continuity have been implicated in the susceptibility to infectious illness. However, prior research has relied on subjective measures of sleep, which are subject to recall bias. The aim of this study was to determine whether sleep, measured behaviorally using wrist actigraphy, predicted cold incidence following experimental viral exposure.

Design, measurements, and results

A total of 164 healthy men and women (age range, 18 to 55 y) volunteered for this study. Wrist actigraphy and sleep diaries assessed sleep duration and sleep continuity over 7 consecutive days. Participants were then quarantined and administered nasal drops containing the rhinovirus, and monitored over 5 days for the development of a clinical cold (defined by infection in the presence of objective signs of illness). Logistic regression analysis revealed that actigraphy- assessed shorter sleep duration was associated with an increased likelihood of development of a clinical cold. Specifically, those sleeping < 5 h (odds ratio [OR] = 4.50, 95% confidence interval [CI], 1.08-18.69) or sleeping between 5 to 6 h (OR = 4.24, 95% CI, 1.08-16.71) were at greater risk of developing the cold compared to those sleeping > 7 h per night; those sleeping 6.01 to 7 h were at no greater risk (OR = 1.66; 95% CI 0.40-6.95). This association was independent of prechallenge antibody levels, demographics, season of the year, body mass index, psychological variables, and health practices. Sleep fragmentation was unrelated to cold susceptibility. Other sleep variables obtained using diary and actigraphy were not strong predictors of cold susceptibility.

Conclusions

Shorter sleep duration, measured behaviorally using actigraphy prior to viral exposure, was associated with increased susceptibility to the common cold.

Rest-activity rhythm (RAR) disturbances are associated with mood disorders. But there remains a need to identify the particular RAR profiles associated with psychiatric symptom dimensions. Establishing such profiles would support the development of tools that track the 24-h sleep-wake phenotypes signaling clinical heterogeneity. We used data-driven clustering to identify RAR profiles in 145 adults aged 36-82 years (mean = 60, standard deviation = 9). Then we evaluated psychiatric symptom dimensions (including positive and negative affect, depressive, manic-hypomanic, panic-agoraphobic, and substance use symptoms) associated with these empirically-derived RAR profiles. Clustering identified three sub-groups characterized, on average, by: (1) earlier and more robust RARs ("earlier/robust," n = 55, 38%); (2) later and irregular RARs ("later/irregular," n = 31, 21%); and (3) later RARs and a narrower active period ("later/narrower," n = 59, 41%). Compared with the "earlier/robust" group: the "later/irregular" group had higher levels of lifetime manic-hypomanic symptoms (β (standard error) = 0.80 (0.22) higher standardized symptom units, p = 0.0004) and lifetime depression symptoms (β (standard error) = 0.73 (0.21) higher standardized symptom units, p = 0.0009); the "later/narrower" group had more lifetime depression symptoms (β (standard error) = 0.48 (0.18) higher standardized symptom units, p = 0.0076). These associations persisted after adjustments for sleep continuity and duration, suggesting that RARs are distinct behavioral correlates of clinical heterogeneity. Longitudinal studies are needed to confirm whether RAR characteristics contribute to the risk of manic and/or depressive episodes, and whether they reflect the consequences of psychiatric disturbance (e.g., on quality of life or disability). Opportunities to monitor or intervene on objectively-assessed RARs could facilitate better mental health related outcomes.

Background

Heart rate variability (HRV) differs markedly by race, yet few studies have evaluated these relationships in women, and none have done so during sleep (sHRV).

Methods

We addressed these gaps by examining sHRV in women of African American, Chinese American, or European American origin or descent (mean [standard deviation] age = 51.2 [2.2] years).

Results

HRV during Stage 2 non-rapid eye movement (NREM) and rapid eye movement (REM) sleep differed significantly by race after adjusting for possible confounders. Normalized high-frequency HRV was significantly lower in European American compared with African American and Chinese American participants (European American NREM = 0.35 [0.01], REM = 0.23 [0.01]; African American NREM = 0.43 [0.02], REM = 0.29 [0.02]; Chinese American NREM = 0.47 [0.03], REM = 0.33 [0.02]; p values <.001). European Americans also exhibited higher low-to-high-frequency HRV ratios during sleep compared with African American and Chinese American women (European American NREM = 2.42 [1.07], REM = 5.05 [1.07]; African American NREM = 1.69 [1.09], REM = 3.51 [1.09]; Chinese American NREM = 1.35 [1.07], REM = 2.88 [1.13]; p values <.001).

Conclusions

Race was robustly related to sHRV. Compared with women of African American or Chinese American origin or descent, European American women exhibited decreased vagally mediated control of the heart during sleep. Prospective data are needed to evaluate whether sHRV, including race differences, predicts cardiovascular disease.

Background

Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (i) establish the predictive ability of a multidimensional self-reported sleep domain for all-cause and cardiovascular mortality in older adults relative to other established risk factors and (ii) to identify which sleep characteristics are most predictive.

Methods

The analytic sample includes N = 8,668 older adults (54% female) aged 65-99 years with self-reported sleep characterization and longitudinal follow-up (≤15.5 years), aggregated from three epidemiological cohorts. We used variable importance (VIMP) metrics from a random survival forest to rank the predictive abilities of 47 measures and domains to which they belong. VIMPs > 0 indicate predictive variables/domains.

Results

Multidimensional sleep was a significant predictor of all-cause (VIMP [99.9% confidence interval {CI}] = 0.94 [0.60, 1.29]) and cardiovascular (1.98 [1.31, 2.64]) mortality. For all-cause mortality, it ranked below that of the sociodemographic (3.94 [3.02, 4.87]), physical health (3.79 [3.01, 4.57]), and medication (1.33 [0.94, 1.73]) domains but above that of the health behaviors domain (0.22 [0.06, 0.38]). The domains were ranked similarly for cardiovascular mortality. The most predictive individual sleep characteristics across outcomes were time in bed, hours spent napping, and wake-up time.

Conclusion

Multidimensional sleep is an important predictor of mortality that should be considered among other more routinely used predictors. Future research should develop tools for measuring multidimensional sleep-especially those incorporating time in bed, napping, and timing-and test mechanistic pathways through which these characteristics relate to mortality.

Purpose

Physical activity has been associated with several individual dimensions of sleep. However, the association between physical activity and sleep health, a construct that emphasizes the multidimensional nature of sleep, has not been explored. This analysis examined the relationship between physical activity and a composite measure of sleep health.

Methods

A total of 114 adults (66% female, 60.3 ± 9.2 years) were included in the analyses. Participants reported daily light-intensity physical activity (LPA) and moderate- and vigorous-intensity physical activity (MVPA) via diary, while wearing a pedometer (Omron HJ-720ITC) to measure daily steps. Sleep health was measured using the RU_SATED questionnaire, which addresses regularity of sleep patterns, satisfaction with sleep, daytime alertness, and sleep timing, efficiency, and duration. Multiple linear regression, binary logistic regression, and analysis of covariance (ANCOVA) were utilized for analyses.

Results

Mean sleep health score was 9.6 ± 2.4 (0 [poor]-12 [good]). Participants reported 62.9 ± 66.0 and 51.2 ± 51.2 min/day of LPA and MVPA, respectively, and took 5585.5 ± 2806.7 steps/day. Greater MVPA was associated with better sleep health (β = 0.27, P = 0.005) and sleep health scores differed between those reporting < 30 min/day and ≥ 60 min/day of MVPA (P = 0.004). Greater MVPA was associated with higher odds of having good sleep satisfaction (OR = 1.58 [1.14-2.20], P < 0.01), timing (OR = 2.07 [1.24-3.46], P < 0.01), and duration (OR = 1.48 [1.02-2.18], P = 0.04). Pedometer-based physical activity and LPA were not related to sleep health or its individual dimensions.

Conclusions

In middle- to older-aged adults, higher-intensity activity, but not lower-intensity or volume of activity, was associated with greater sleep health. These data suggest that physical activity intensity may be important for sleep health.

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.

Study objectives

To identify actigraphy sleep health profiles in older men (Osteoporotic Fractures in Men Study; N = 2640) and women (Study of Osteoporotic Fractures; N = 2430), and to determine whether profile predicts mortality.

Methods

We applied a novel and flexible clustering approach (Multiple Coalesced Generalized Hyperbolic mixture modeling) to identify sleep health profiles based on actigraphy midpoint timing, midpoint variability, sleep interval length, maintenance, and napping/inactivity. Adjusted Cox models were used to determine whether profile predicts time to all-cause mortality.

Results

We identified similar profiles in men and women: High Sleep Propensity [HSP] (20% of women; 39% of men; high napping and high maintenance); Adequate Sleep [AS] (74% of women; 31% of men; typical actigraphy levels); and Inadequate Sleep [IS] (6% of women; 30% of men; low maintenance and late/variable midpoint). In women, IS was associated with increased mortality risk (Hazard Ratio [HR] = 1.59 for IS vs. AS; 1.75 for IS vs. HSP). In men, AS and IS were associated with increased mortality risk (1.19 for IS vs. HSP; 1.22 for AS vs. HSP).

Conclusions

These findings suggest several considerations for sleep-related interventions in older adults. Low maintenance with late/variable midpoint is associated with increased mortality risk and may constitute a specific target for sleep health interventions. High napping/inactivity co-occurs with high sleep maintenance in some older adults. Although high napping/inactivity is typically considered a risk factor for deleterious health outcomes, our findings suggest that it may not increase risk when it occurs in combination with high sleep maintenance.

Sleep is multidimensional, with domains including duration, timing, continuity, regularity, rhythmicity, quality, and sleepiness/alertness. Individual sleep characteristics representing these domains are known to predict health outcomes. However, most studies consider sleep characteristics in isolation, resulting in an incomplete understanding of which sleep characteristics are the strongest predictors of health outcomes. We applied three multivariable approaches to robustly determine which sleep characteristics increase mortality risk in the osteoporotic fractures in men sleep study. In total, 2,887 men (mean 76.3 years) completed relevant assessments and were followed for up to 11 years. One actigraphy or self-reported sleep characteristic was selected to represent each of seven sleep domains. Multivariable Cox models, survival trees, and random survival forests were applied to determine which sleep characteristics increase mortality risk. Rhythmicity (actigraphy pseudo-F statistic) and continuity (actigraphy minutes awake after sleep onset) were the most robust sleep predictors across models. In a multivariable Cox model, lower rhythmicity (hazard ratio, HR [95%CI] =1.12 [1.04, 1.22]) and lower continuity (1.16 [1.08, 1.24]) were the strongest sleep predictors. In the random survival forest, rhythmicity and continuity were the most important individual sleep characteristics (ranked as the sixth and eighth most important among 43 possible sleep and non-sleep predictors); moreover, the predictive importance of all sleep information considered simultaneously followed only age, cognition, and cardiovascular disease. Research within a multidimensional sleep health framework can jumpstart future research on causal pathways linking sleep and health, new interventions that target specific sleep health profiles, and improved sleep screening for adverse health outcomes.

Sudden cardiac death (SCD) is the sudden, unexpected death due to abrupt loss of heart function secondary to cardiovascular disease. In certain populations living with cardiovascular disease, SCD follows a distinct 24-hour pattern in occurrence, suggesting day/night rhythms in behavior, the environment, and endogenous circadian rhythms result in daily spans of increased vulnerability. The National Heart, Lung, and Blood Institute convened a workshop, Understanding Circadian Mechanisms of Sudden Cardiac Death to identify fundamental questions regarding the role of the circadian rhythms in SCD. Part 2 summarizes research gaps and opportunities in the areas of population and clinical research identified in the workshop. Established research supports a complex interaction between circadian rhythms and physiological responses that increase the risk for SCD. Moreover, these physiological responses themselves are influenced by several biological variables, including the type of cardiovascular disease, sex, age, and genetics, as well as environmental factors. The emergence of new noninvasive biotechnological tools that continuously measure key cardiovascular variables, as well as the identification of biomarkers to assess circadian rhythms, hold promise for generating large-scale human data sets that will delineate which subsets of individuals are most vulnerable to SCD. Additionally, these data will improve our understanding of how people who suffer from circadian disruptions develop cardiovascular diseases that increase the risk for SCD. Emerging strategies to identify new biomarkers that can quantify circadian health (eg, environmental, behavioral, and internal misalignment) may lead to new interventions and therapeutic targets to prevent the progression of cardiovascular diseases that cause SCD.

Sudden cardiac death (SCD), the unexpected death due to acquired or genetic cardiovascular disease, follows distinct 24-hour patterns in occurrence. These 24-hour patterns likely reflect daily changes in arrhythmogenic triggers and the myocardial substrate caused by day/night rhythms in behavior, the environment, and endogenous circadian mechanisms. To better address fundamental questions regarding the circadian mechanisms, the National Heart, Lung, and Blood Institute convened a workshop, Understanding Circadian Mechanisms of Sudden Cardiac Death. We present a 2-part report of findings from this workshop. Part 1 summarizes the workshop and serves to identify research gaps and opportunities in the areas of basic and translational research. Among the gaps was the lack of standardization in animal studies for reporting environmental conditions (eg, timing of experiments relative to the light dark cycle or animal housing temperatures) that can impair rigor and reproducibility. Workshop participants also pointed to uncertainty regarding the importance of maintaining normal circadian rhythmic synchrony and the potential pathological impact of desynchrony on SCD risk. One related question raised was whether circadian mechanisms can be targeted to reduce SCD risk. Finally, the experts underscored the need for studies aimed at determining the physiological importance of circadian clocks in the many different cell types important to normal heart function and SCD. Addressing these gaps could lead to new therapeutic approaches/molecular targets that can mitigate the risk of SCD not only at certain times but over the entire 24-hour period.