Heightened sleep propensity: a novel and high-risk sleep health phenotype in older adults

The existing state of knowledge about sleep health in community-dwelling older persons - a scoping review

OBJECTIVES

It is widely known that sleep disorders are a common problem among older persons. Few reviews have described current knowledge about the holistic concept of sleep health of community-dwelling older people.

AIM

This study aimed to describe the current state of knowledge and identify research gaps concerning sleep health among community-dwelling older persons.

METHOD

We conducted a scoping review. Searches were conducted in three databases (Medline, CINAHL, and PsycINFO) to identify scientific articles including outcomes with all five sleep health dimensions (sleep duration, sleep continuity, timing, wakefulness/daytime sleepiness, and sleep quality) among community-dwelling older persons aged ≥65 years. Eight articles were included from a total of 1826 hits, with sample sizes between 1413 and 6485.

RESULTS

The sleep health outcomes of community-dwelling older adults differed between the sexes. Older persons with at least two or more poor sleep health dimensions might have increased risk for depression, higher healthcare costs and mortality, while self-reported better sleep health might be associated with lower odds of frailty.

CONCLUSION

Future research is needed to confirm the findings by investigating the multidimensional concept of sleep health in a general older population. The identified knowledge gaps are how persons ≥80 years' experience their sleep health, and how sleep medicine is prescribed to treat sleep problems in persons ≥80 years in different care contexts.

Association of Sleep Pattern and Genetic Susceptibility with Obstructive Sleep Apnea: A Prospective Analysis of the UK Biobank

Purpose

The prevalence of obstructive sleep apnea (OSA) is high worldwide. This study aimed to quantify the relationship between the incidence of OSA and sleep patterns and genetic susceptibility.

Methods

A total of 355,133 white British participants enrolled in the UK Biobank between 2006 and 2010 with follow-up data until September 2021 were recruited. We evaluated sleep patterns using a customized sleep scoring method based on the low-risk sleep phenotype, defined as follows: morning chronotype, 7-8 hours of sleep per day, never/rarely experience insomnia, no snoring, no frequent daytime sleepiness, never/rarely nap, and easily getting up early. The polygenic risk score was calculated to assess genetic susceptibility to OSA. Cox proportional hazard models were used to evaluate the associations between OSA and sleep patterns and genetic susceptibility.

Results

During a mean follow-up of 12.57 years, 4618 participants were diagnosed with OSA (age: 56.83 ± 7.69 years, women: 31.3%). Compared with those with a poor sleep pattern, participants with a normal (HR: 0.42, 95% CI: 0.38-0.46), ideal (HR: 0.21, 95% CI: 0.19-0.24), or optimal (HR: 0.15, 95% CI: 0.12-0.18) sleep pattern were significantly more likely to have OSA. The genetic susceptibility of 173,239 participants was calculated, and the results showed that poor (HR: 3.67, 95% CI: 2.95-4.57) and normal (HR: 1.89, 95% CI: 1.66-2.16) sleep patterns with high genetic susceptibility can increase the risk for OSA.

Conclusion

This large-scale prospective study provides evidence suggesting that sleep patterns across seven low-risk sleep phenotypes may protect against OSA in individuals with varying degrees of genetic susceptibility.

Development and validation of a novel sleep health score in the sleep heart health study

BACKGROUND

There is a lack of consensus in evaluating multidimensional sleep health, especially concerning its implication for mortality. A validated multidimensional sleep health score is the foundation of effective interventions.

METHODS

We obtained data from 5706 participants in the Sleep Heart Health Study. First, random forest-recursive feature elimination algorithm was used to select potential predictive variables. Second, a sleep composite score was developed based on the regression coefficients from a Cox proportional hazards model evaluating the associations between selected sleep-related variables and mortality. Last, we validated the score by constructing Cox proportional hazards models to assess its association with mortality.

RESULTS

The mean age of participants was 63.2 years old, and 47.6% (2715/5706) were male. Six sleep variables, including average oxygen saturation (%), spindle density (C3), sleep efficiency (%), spindle density (C4), percentage of fast spindles (%) and percentage of rapid eye movement (%) were selected to construct this multidimensional sleep health score. The average sleep composite score in participants was 6.8 of 22 (lower is better). Participants with a one-point increase in sleep composite score had an 10% higher risk of death (hazard ratio = 1.10, 95% confidence interval: 1.08-1.12).

CONCLUSIONS

This study constructed and validated a novel multidimensional sleep health score to better predict death based on sleep, with significant associations between sleep composite score and all-cause mortality. Integrating questionnaire information and sleep microstructures, our sleep composite score is more appropriately applied for mortality risk stratification.

Ten-Year Stability of an Insomnia Sleeper Phenotype and Its Association With Chronic Conditions

OBJECTIVE

To identify distinct sleep health phenotypes in adults, examine transitions in sleep health phenotypes over time, and subsequently relate these to the risk of chronic conditions.

METHODS

A national sample of adults from the Midlife in the United States study ( N = 3683) provided longitudinal data with two time points (T1: 2004-2006, T2: 2013-2017). Participants self-reported on sleep health (regularity, satisfaction, alertness, efficiency, duration) and the number and type of chronic conditions. Covariates included age, sex, race, education, education, partnered status, number of children, work status, smoking, alcohol, and physical activity.

RESULTS

Latent transition analysis identified four sleep health phenotypes across both time points: good sleepers, insomnia sleepers, weekend catch-up sleepers, and nappers. Between T1 and T2, the majority (77%) maintained their phenotype, with the nappers and insomnia sleepers being the most stable. In fully adjusted models with good sleepers at both time points as the reference, being an insomnia sleeper at either time point was related to having an increased number of total chronic conditions by 28%-81% at T2, adjusting for T1 conditions. Insomnia sleepers at both time points were at 72%-188% higher risk for cardiovascular disease, diabetes, depression, and frailty. Being a napper at any time point related to increased risks for diabetes, cancer, and frailty. Being a weekend catch-up sleeper was not associated with chronic conditions. Those with lower education and unemployed were more likely to be insomnia sleepers; older adults and retirees were more likely to be nappers.

CONCLUSION

Findings indicate a heightened risk of chronic conditions involved in suboptimal sleep health phenotypes, mainly insomnia sleepers.

Effect of daily discrimination on naturalistic sleep health features in young adults

OBJECTIVE

Racial inequities in sleep health are well documented and may be partially attributable to discrimination experiences. However, the effects of acute discrimination experiences on same-night sleep health are understudied. We quantified naturalistic discrimination experiences captured using ecological momentary assessment (EMA) and examined whether reporting discrimination on a given day predicted sleep health that night.

METHOD

Participants completed baseline assessments and a 17-day EMA protocol, with text prompts delivered four times daily to collect discrimination experiences. Seven different daily sleep characteristics were ascertained each morning. Discrimination reasons (e.g., because of my racial identity) were reported by participants and categorized into any, racial, or nonracial discrimination. Outcomes included the seven sleep diary characteristics. We fit generalized linear mixed effects models for each sleep outcome and discrimination category, controlling for key covariates.

RESULTS

The analytic sample included 116 self-identified Black and White individuals (48% Black, 71% assigned female at birth, average age = 24.5 years). Among Black participants, race-based discrimination was associated with a 0.5-hr reduction in total sleep time (TST). Among White individuals, nonracial discrimination was associated with a 0.6-hr reduction in TST, an earlier sleep offset, and reduced sleep efficiency (partly attributable to more nighttime awakenings).

CONCLUSIONS

Young adults may sleep worse on nights after experiencing discrimination, and different types of discrimination affect different sleep outcomes for Black and White individuals. Future studies may consider developing treatments that account for different sleep vulnerabilities for people experiencing discrimination on a given day. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multidimensional Sleep Health Problems Across Middle and Older Adulthood Predict Early Mortality

BACKGROUND

Having multiple sleep problems is common in adulthood. Yet, most studies have assessed single sleep variables at one timepoint, potentially misinterpreting health consequences of co-occurring sleep problems that may change over time. We investigated the relationship between multidimensional sleep health across adulthood and mortality.

METHODS

Participants from the Midlife in the United States Study reported sleep characteristics in 2004-2006 (MIDUS-2; M2) and in 2013-2014 (MIDUS-3; M3). We calculated a composite score of sleep health problems across 5 dimensions: Regularity, Satisfaction, Alertness, Efficiency, and Duration (higher = more problems). Two separate models for baseline sleep health (n = 5 140; median follow-up time = 15.3 years) and change in sleep health (n = 2 991; median follow-up time = 6.4 years) to mortality were conducted. Cox regression models controlled for sociodemographics and key health risk factors (body mass index, smoking, depressive symptoms, diabetes, and hypertension).

RESULTS

On average, 88% of the sample reported having one or more sleep health problems at M2. Each additional sleep health problem at M2 was associated with 12% greater risk of all-cause mortality (hazard ratio [HR] = 1.12, 95% confidence interval [CI] = 1.04-1.21), but not heart disease-related mortality (HR = 1.14, 95% CI = 0.99-1.31). An increase in sleep health problems from M2 to M3 was associated with 27% greater risk of all-cause mortality (HR = 1.27, 95% CI = 1.005-1.59), and 153% greater risk of heart disease mortality (HR = 2.53, 95% CI = 1.37-4.68).

CONCLUSIONS

More sleep health problems may increase the risk of early mortality. Sleep health in middle and older adulthood is a vital sign that can be assessed at medical checkups to identify those at greater risk.

Different dimensions of daytime sleepiness predicted mortality in older adults: Sex and muscle power-specific risk in Yilan Study, Taiwan

OBJECTIVES

This study aimed to investigate the relationship between daytime sleepiness and mortality risk among older adults. The moderating effects of sex and physical function were examined.

METHODS

This 9-year follow-up study was conducted with community-dwelling individuals aged ≥65 years. Daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). Exploratory factor analysis (EFA) was used to examine the ESS factors. Handgrip strength was measured to assess physical function, and the highest quartile was defined as good muscle power. Cox regression analysis was used to estimate the 9-year all-cause mortality risk. The interaction terms were examined to evaluate their moderating effect.

RESULTS

In total, 2588 individuals participated in the study. The EFA explored two factors: the passive factor (PF) and the active factor (AF). After controlling for various covariates, the cutoff-defined daytime sleepiness (ESS≥11), total raw scores, and factor scores of the ESS all failed to predict mortality risk. The 3-way interaction terms showed statistical significance in terms of [sex × PF × muscle power (p = 0.03)] but not for [sex × AF × muscle power (p = 0.11)]. Specifically, PF predicted mortality risk in women with good muscle power (hazard ratio (HR): 1.48; 95 % confidence interval (CI): 1.04-2.10), which is female-specific. In contrast, AF predicted mortality risk only in men with good muscle power (HR: 1.35; 95 % CI: 1.02-1.78).

CONCLUSIONS

The ESS-measured daytime sleepiness in older adults is multidimensional. The mortality risk for each dimension was determined based on sex and physical function.

Race and sex differences in the longitudinal changes in multidimensional self-reported sleep health characteristics in aging older adults

OBJECTIVES

We examined within-individual changes in self-reported sleep health as community-dwelling older adults age as well as potential differences in these changes by self-reported sex and racial identity.

METHODS

Participants were from the United States and enrolled in the Rush Memory and Aging Project, Minority Aging Research Study, or Religious Orders Study (N = 3539, 20% Black, 75% female, mean 78years [range 65-103]), and they received annual, in-person clinical evaluations (median 5 visits [range 1-27]). A sleep health composite score measured the number of poor sleep characteristics among satisfaction, daytime sleepiness, efficiency, and duration. Mixed effects models estimated associations of age, race, sex, and their interactions on the composite and individual sleep measures, accounting for key confounders.

RESULTS

As they aged, Black participants shifted from reporting two poor sleep characteristics to one poor sleep characteristic, while White participants shifted from one poor characteristic to two. Regardless of age, sex, and race, participants reported that they "often" felt satisfied with their sleep and "sometimes" had trouble staying asleep. Females over age 85 and males of all ages reported the most daytime sleepiness, and older White participants (>age 90) reported the most difficulty falling asleep.

CONCLUSIONS

Although self-reported sleep characteristics were typically stable across age, identifying race and sex differences in self-reported sleep health can help guide future research to understand the mechanisms that underlie these differences.

Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study

OBJECTIVES

The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor.

METHODS

Wrist actigraphy measures of participants (N = 289, Mage = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment).

RESULTS

Exploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (β [95% confidence interval] = 0.63 [0.19, 1.08]).

CONCLUSIONS

Actigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.

Associations between sleep health and obesity and weight change in adults: The Daily24 Multisite Cohort Study

OBJECTIVES

To examine cross-sectional and longitudinal associations of individual sleep domains and multidimensional sleep health with current overweight or obesity and 5-year weight change in adults.

METHODS

We estimated sleep regularity, quality, timing, onset latency, sleep interruptions, duration, and napping using validated questionnaires. We calculated multidimensional sleep health using a composite score (total number of "good" sleep health indicators) and sleep phenotypes derived from latent class analysis. Logistic regression was used to examine associations between sleep and overweight or obesity. Multinomial regression was used to examine associations between sleep and weight change (gain, loss, or maintenance) over a median of 1.66 years.

RESULTS

The sample included 1016 participants with a median age of 52 (IQR = 37-65), who primarily identified as female (78%), White (79%), and college-educated (74%). We identified 3 phenotypes: good, moderate, and poor sleep. More regularity of sleep, sleep quality, and shorter sleep onset latency were associated with 37%, 38%, and 45% lower odds of overweight or obesity, respectively. The addition of each good sleep health dimension was associated with 16% lower adjusted odds of having overweight or obesity. The adjusted odds of overweight or obesity were similar between sleep phenotypes. Sleep, individual or multidimensional sleep health, was not associated with weight change.

CONCLUSIONS

Multidimensional sleep health showed cross-sectional, but not longitudinal, associations with overweight or obesity. Future research should advance our understanding of how to assess multidimensional sleep health to understand the relationship between all aspects of sleep health and weight over time.

The association between multidimensional sleep health and gestational weight gain

BACKGROUND

Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed.

OBJECTIVES

This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG).

METHODS

Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined 'healthy' sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD) and high (>+1 SD).

RESULTS

Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null.

CONCLUSIONS

Future research should determine whether sleep health is an intervention target for healthy GWG.

The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study

Background

Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG).

Methods

We conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined "healthy" sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD), and high (>+1 SD).

Results

Nearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG).

Conclusions

Multidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG.

Synopsis

Study question: What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What's already known?: Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?: We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain.

Sleep and circadian rhythm profiles in seasonal depression

Sleep and circadian rhythm disruptions are symptoms of, and hypothesized underlying mechanisms in, seasonal depression. Discrepant observational findings and mixed responses to sleep/circadian-based treatments suggest heterogenous sleep and circadian disruptions in seasonal depression, despite these disruptions historically conceptualized as delayed circadian phase and hypersomnia. This study used a data-driven cluster analysis to characterize sleep/circadian profiles in seasonal depression to identify treatment targets for future interventions. Biobehavioral measures of sleep and circadian rhythms were assessed during the winter in individuals with Seasonal Affective Disorder (SAD), subsyndromal-SAD (S-SAD), or nonseasonal, never depressed controls (total sample N = 103). The following variables were used in the cluster analysis: circadian phase (from dim light melatonin onset), midsleep timing, total sleep time, sleep efficiency, regularity of midsleep timing, and nap duration (all from wrist actigraphy). Sleep and circadian variables were compared across clusters and controls. Despite limited sleep/circadian differences between diagnostic groups, there were two reliable (Jaccard Coefficients >0.75) sleep/circadian profiles in SAD/S-SAD individuals: a 'Disrupted sleep' cluster, characterized by irregular and fragmented sleep and an 'Advanced' cluster, characterized by early sleep and circadian timing and longer total sleep times (>7.5 h). Clusters did not differ by depression severity. Midsleep correlated with DLMO (r = 0.56), irregularity (r = 0.3), and total sleep time (r = -0.27). Sleep and circadian disruptions in seasonal depression are not uniformly characterized by hypersomnia and circadian phase delay. Presence of distinct sleep and circadian subgroups in seasonal depression may predict successful treatment response. Prospective assessment and tailoring of individual sleep and circadian disruptions may reduce treatment failures.

Sleep health, its intraindividuality, and perceived stress in college students during the COVID-19 pandemic

Objective: To describe the changes in sleep health domains and examine the associations between the repeated measures and intraindividual variability (IIV) of these domains and perceived stress. Participants: A diverse racial and ethnic group of first-year college students (N = 23, 78.3% female, aged 17-18) attending in-person classes during the COVID-19 pandemic. Methods: Sleep health domains were determined using 7-day wrist actigraph and daily sleep diaries, and perceived stress scale was completed at 1-month intervals across 3 months. Results: Sleep timing, regularity, and alertness during daytime demonstrated statistically significant changes between three timepoints. Greater stress was associated with more irregularity (B = 2.25 [.87-3.62], p < .001), more dissatisfaction in sleep (B = .04 [.02-.19], p < .01), alertness during daytime (B = .18 [.05-.31], p < .001), and greater IIV (ie, fluctuations) in sleep satisfaction (B = .083 [.02, .15], p < .01). Conclusion: These findings offer insights for future researchers to facilitate intervention development to promote mental and sleep health among college students.

Cardiovascular risks and sociodemographic correlates of multidimensional sleep phenotypes in two samples of US adults

Study Objectives

Sleep is a modifiable risk factor for cardiovascular conditions. Holistic examination of within-person, multidimensional sleep patterns may offer more detailed information about the sleep-cardiovascular condition link, including who is more vulnerable to both. This study aimed to identify common sleep phenotypes in adulthood, establish the validity of the phenotypes in relation to cardiovascular conditions, and explore sociodemographic and background characteristics of the phenotypes.

Methods

Across two independent samples of adults (N ₁ = 4600; N ₂ = 2598) from the Midlife in the United States Study, latent class analysis (LCA) extracted sleep phenotypes using five key self-reported sleep dimensions. Log-binomial regression was used to determine whether sleep phenotypes differentially predicted cardiovascular conditions, adjusting for known risk factors. LCA with covariates was used to compare sociodemographic characteristics of the identified sleep phenotypes.

Results

Four sleep phenotypes were identified consistently across the two samples: good sleepers, nappers, dissatisfied/inefficient sleepers, and irregular sleepers. Compared to good sleepers (reference), dissatisfied/inefficient sleepers exhibited a higher risk of cardiovascular conditions in both samples (RR _Sample1: 29%, RR _Sample2: 53%) and consisted of relatively more racial/ethnic minorities. Nappers exhibited a higher risk of cardiovascular conditions in one sample (RR _Sample1: 38%) and consisted of more women and older adults. Irregular sleepers exhibited no significantly different cardiovascular risk and were relatively younger.

Conclusions

Common sleep phenotypes in adulthood exhibit differential risks for cardiovascular conditions. Cooccurring sleep dissatisfaction and inefficiency, in particular, may relate to increased risk of cardiovascular conditions. Certain sociodemographic groups (racial minorities, women, older adults) disproportionately fit within high-risk sleep phenotypes.

Actigraphy-derived sleep health profiles and mortality in older men and women

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 and Risk for Metabolic Syndrome, Hypertension, Diabetes and Obesity Among Community-Dwelling Older Adults

Older adults often face a variety of health problems that are found less frequently in younger populations. Metabolic syndrome and other related diseases are common due to a variety of age and lifestyle factors. Sleep, often operationalized only as duration, quality, or apnea diagnosis, is associated with worse health outcomes across the lifespan. However, sleep is multi-faceted and may require a collection of measures in order to reflect this. This study examined a suite of self-reported sleep habits (risk for sleep apnea, night time duration, nap duration, quality, timing, and consistency of duration and timing) and physiological data in a sample of 144 older adults. Sleep-related variables as a group predicted risk for metabolic syndrome, hypertension, and diabetes but was not a clear predictor of obesity. Of the individual measures, risk for apnea and consistency of sleep duration throughout the week predicted risk for metabolic syndrome (apnea b = .64, p < .05; duration inconsistencies b = .22, p < .05). The findings of the study suggest that greater consistency in sleep schedules may benefit the health of older adult populations' risk for these disorders.

Multidimensional sleep health in a diverse, aging adult cohort: Concepts, advances, and implications for research and intervention

PURPOSE

To illustrate 2 frameshifts of multidimensional sleep health: i) use of composite sleep metrics; and ii) the correlations among sleep dimensions.

PARTICIPANTS

735 adults of diverse backgrounds aged <65 years who participated in the Multi-Ethnic Study of Atherosclerosis.

MEASURES

In-home polysomnography, 7-day wrist actigraphy, and validated questionnaires.

METHODS

The Buysse Ru SATED model-sleep regularity, satisfaction, alertness, timing, efficiency, duration-was operationalized, then extended by including additional measures of sleep architecture and sleep apnea from polysomnography and difficulties initiating sleep from questionnaire and sleep onset latency and duration [ir]regularity from actigraphy. We dichotomized sleep variables, operationalizing optimal and nonoptimal ranges as 1 and 0, respectively, summed into a sleep health score, and computed global sleep health scores via principal components analysis.

FINDINGS

Participants showed low prevalence of sleep regularity in timing (<30 minutes standard deviation [SD]; 21.4% favorable) and duration (<60 minutes SD; 36.9%). Although 62.7% of participants demonstrated favorable sleep duration by actigraphy, few met criteria for favorable levels of % N3 (11.4%) or %R (34.1%). The average Sleep Health Score was 5.6 of 13 (higher is better). Sleep variables were variably intercorrelated (r = 0 to r = -0.72). The first principal component for each operationalization of sleep health was interpretable as a "health" score; all summary scores captured variable but systematic shifts towards more favorable sleep in each sleep variable.

CONCLUSIONS

Multidimensional sleep health can be measured by complementary composite scores as well as consideration of multiple individual dimensions.

Identifying diverse forms of (un)healthy sleep: Sleep profiles differentiate adults' psychological and physical well-being

RATIONALE

Sleep health is best described by the co-occurrence of various dimensions (e.g., regularity, daytime alertness, satisfaction, efficiency, duration) but is rarely measured this way. Information is needed regarding common within-person patterns of sleep characteristics among adults and their relative healthiness.

OBJECTIVE

To deepen understanding of healthy and unhealthy sleep, the present study aimed to uncover multidimensional sleep profiles in adults and their associations with a variety of psychological and physical well-being outcomes.

METHODS

Survey data from 4622 adults who participated in the Midlife in the United States (MIDUS) project was used to identify latent sleep profiles across five core sleep dimensions. Adjusting for individual sleep dimensions and sociodemographic covariates, General Linear Models were used to test the associations of sleep profile membership with hedonic and eudemonic well-being and chronic physical conditions.

RESULTS

Four latent sleep profiles were revealed, good sleepers, sufficient but irregular sleepers, nappers, and short, dissatisfied, and inefficient sleepers. The profiles differentially related to well-being outcomes above and beyond individual sleep dimensions and sociodemographic covariates. Good sleepers generally reported the best outcomes, and short, dissatisfied, and inefficient sleepers generally reported the worst outcomes.

CONCLUSION

Four common sleep profiles describe adults' holistic sleep experiences and predict a variety of well-being outcomes beyond other known predictors. In adulthood, healthy sleep may involve sufficient sleep across all dimensions whereas unhealthy sleep may involve insufficient sleep across three key dimensions: duration, satisfaction, and efficiency.

Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework

Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measures), self-reported sleep (three), health (eight), health behaviours (four), and sociodemographic factors (five). A 10-fold repeated internal validation (WSCS and SHHS combined) and external validation (training in SHHS; testing in WSCS) were used to compute unbiased variable importance metrics and associated p values. We observed that health, sociodemographic factors, and PSG-assessed sleep domains predicted mortality using both external validation and repeated internal validation. The PSG-assessed sleep efficiency and the percentage of sleep time with oxygen saturation <90% were among the most predictive individual measures. Multivariable Cox regression also revealed the PSG-assessed sleep domain to be predictive, with very low sleep efficiency and high hypoxaemia conferring the highest risk. These findings, coupled with the emergence of new low-burden technologies for objectively assessing sleep and overnight oxygen saturation, suggest that consideration of physiological sleep measures may improve risk screening.

Multidimensional sleep health domains in older men and women: an actigraphy factor analysis

The multidimensional sleep health framework emphasizes that sleep can be characterized across several domains, with implications for developing novel sleep treatments and improved prediction and health screening. However, empirical evidence regarding the domains and representative measures that exist in actigraphy-assessed sleep is lacking. We aimed to establish these domains and representative measures in older adults by examining the factor structure of 28 actigraphy-derived sleep measures from 2,841 older men from the Osteoporotic Fractures in Men Sleep Study and, separately, from 2,719 older women from the Study of Osteoporotic Fractures. Measures included means and standard deviations of actigraphy summary measures and estimates from extended cosine models of the raw actigraphy data. Exploratory factor analyses revealed the same five factors in both sexes: Timing (e.g. mean midpoint from sleep onset to wake-up), Efficiency (e.g. mean sleep efficiency), Duration (e.g. mean minutes from sleep onset to wake-up), Sleepiness/Wakefulness (e.g. mean minutes napping and amplitude of rhythm), and Regularity (e.g. standard deviation of the midpoint). Within each sex, confirmatory factor analyses confirmed the one-factor structure of each factor and the entire five-factor structure (Comparative Fit Index and Tucker-Lewis Index ≥ 0.95; Root Mean Square Error of Approximation 0.08-0.38). Correlation magnitudes among factors ranged from 0.01 to 0.34. These findings demonstrate the validity of conceptualizing actigraphy sleep as multidimensional, provide a framework for selecting sleep health domains and representative measures, and suggest targets for behavioral interventions. Similar analyses should be performed with additional measures of rhythmicity, other age ranges, and more racially/ethnically diverse samples.

The Different Faces of Insomnia

Objectives: The identification of clinically relevant subtypes of insomnia is important. Including a comprehensive literature review, this study also introduces new phenotypical relevant parameters by describing a specific insomnia cohort. Methods: Patients visiting the sleep center and indicating self-reported signs of insomnia were examined by a sleep specialist who confirmed an insomnia diagnosis. A 14-item insomnia questionnaire on symptoms, progression, sleep history and treatment, was part of the clinical routine. Results: A cohort of 456 insomnia patients was described (56% women, mean age 52 ± 16 years). They had suffered from symptoms for about 12 ± 11 years before seeing a sleep specialist. About 40-50% mentioned a trigger (most frequently psychological triggers), a history of being bad sleepers to begin with, a family history of sleep problems, and a negative progression of insomnia. Over one third were not able to fall asleep during the day. SMI (sleep maintenance insomnia) symptoms were most frequent, but only prevalence of EMA (early morning awakening) symptoms significantly increased from 40 to 45% over time. Alternative non-medical treatments were effective in fewer than 10% of cases. Conclusion: Our specific cohort displayed a long history of suffering and the sleep specialist is usually not the first point of contact. We aimed to describe specific characteristics of insomnia with a simple questionnaire, containing questions (e.g., ability to fall asleep during the day, effects of non-medical therapy methods, symptom stability) not yet commonly asked and of unknown clinical relevance as yet. We suggest adding them to anamnesis to help differentiate the severity of insomnia and initiate further research, leading to a better understanding of the severity of insomnia and individualized therapy. This study is part of a specific Research Topic introduced by Frontiers on the heterogeneity of insomnia and its comorbidity and will hopefully inspire more research in this area.

Twenty-Four Hour Activity and Sleep Profiles for Adults Living with Arthritis: Habits Matter

OBJECTIVE

To identify 24-hour activity-sleep profiles in adults with arthritis and explore factors associated with profile membership.

METHODS

Our study comprised a cross-sectional cohort and used baseline data from 2 randomized trials studying activity counseling for people with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), or knee osteoarthritis (OA). Participants wore activity monitors for 1 week and completed surveys for demographic information, mood (Patient Health Questionnaire 9), and sitting and walking habits (Self-Reported Habit Index). A total of 1,440 minutes/day were stratified into minutes off body (activity unknown), sleeping, resting, nonambulatory, and intermittent or purposeful ambulation. Latent class analysis determined cluster numbers; baseline-category multinomial logit regression identified factors associated with cluster membership.

RESULTS

Our cohort included 172 individuals, including 51% with RA, 30% with OA, and 19% with SLE. We identified 4 activity-sleep profiles (clusters) that were characterized primarily by differences in time in nonambulatory activity: high sitters (6.9 hours sleep, 1.6 hours rest, 13.2 hours nonambulatory activity, and 1.6 hours intermittent and 0.3 hours purposeful walking), low sleepers (6.5 hours sleep, 1.2 hours rest, 12.2 hours nonambulatory activity, and 3.3 hours intermittent and 0.6 hours purposeful walking), high sleepers (8.4 hours sleep, 1.9 hours rest, 10.4 hours nonambulatory activity, and 2.5 hours intermittent and 0.3 hours purposeful walking), and balanced activity (7.4 hours sleep, 1.5 hours sleep, 9.4 hours nonambulatory activity, and 4.4 hours intermittent and 0.8 hours purposeful walking). Younger age (odds ratio [OR] 0.95 [95% confidence interval (95% CI) 0.91-0.99]), weaker occupational sitting habit (OR 0.55 [95% CI 0.41-0.76]), and stronger walking outside habit (OR 1.43 [95% CI 1.06-1.91]) were each associated with balanced activity relative to high sitters.

CONCLUSION

Meaningful subgroups were identified based on 24-hour activity-sleep patterns. Tailoring interventions based on 24-hour activity-sleep profiles may be indicated, particularly in adults with stronger habitual sitting or weaker walking behaviors.