Multidimensional sleep health is not cross-sectionally or longitudinally associated with adiposity in the Study of Women's Health Across the Nation (SWAN)

The relationships between multidimensional sleep health and work productivity in individuals with neurological conditions

Numerous studies have reported the negative impacts of poor sleep on work productivity in the general population. However, despite the known sleep issues that individuals living with neurological conditions experience, no study has explored its impact on their work productivity. Sleep health is a concept that includes multiple domains of sleep, measured with a combination of objective and subjective measures. Therefore, this study aimed to ascertain the associations between sleep health and its domains and work productivity in individuals with neurological conditions. Sleep health domains were determined through actigraphy data collected over 1 week and sleep questionnaires. Work productivity was assessed via the Work Productivity and Activity Impairment Questionnaire. A comparison of sleep health scores between demographic variables was performed using Mann-Whitney U and Kruskal-Wallis tests. Associations between the sleep health domains and work productivity were performed using linear regression models. There were no significant differences in sleep health scores between sex, smoking status, education level, employment status or any work productivity domain. Individuals with non-optimal sleep timing had greater absenteeism (22.99%) than the optimal group. Individuals with non-optimal sleep quality had an increase in presenteeism (30.85%), work productivity loss (26.44%) and activity impairment (25.81%) compared to those in the optimal group. The findings from this study highlight that self-reported sleep quality has the largest impact on work productivity. Improving individuals' sleep quality through triage for potential sleep disorders or improving their sleep hygiene (sleep behaviour and environment) may positively impact work productivity.

Short sleep duration is associated with higher risk of central obesity in adults: A systematic review and meta-analysis of prospective cohort studies

Background and Objective

The relationship between sleep duration and obesity has been the focus of numerous investigations. This systematic review and meta-analysis of prospective cohort studies aimed to assess the relationship between sleep duration, abdominal obesity, and body composition.

Methods

PubMed, Scopus, and Web of Science were searched until February 2024. Cohort studies that assessed the relationship between sleep duration at night and central obesity measures or body composition indices in adults were included. The quality of studies was assessed using the Newcastle-Ottawa scale. Random-effects meta-analysis was conducted on studies that reported risk ratio (RR) and 95% confidence intervals (CIs).

Results

Eighteen studies were eligible to be included. Eleven out of the 18 studies were not included in the analysis as 10 studies did not report RR, and in one study, the definition of short and normal sleep duration was different from others. The results of the meta-analysis indicated that short sleep duration was significantly associated with abdominal obesity (RR = 1.08; 95% CI: 1.04-1.12; I 2 = 49.1%, n = 7), but long sleep duration was not (RR = 1.02; 95% CI: 0.83-1.24; I 2 = 98.2%, n = 6).

Conclusions

Short sleep duration was associated with a slightly higher risk of central obesity, while long sleep duration was not.

Objective and Subjective Intraindividual Variability in Sleep: Predisposing Factors and Health Consequences

OBJECTIVE

We investigated the factors that predispose or precipitate greater intraindividual variability (IIV) in sleep. We further examined the potential consequences of IIV on overall sleep quality and health outcomes, including whether these relationships were found in both self-reported and actigraphy-measured sleep IIV.

METHODS

In Study 1, 699 US adults completed a Sleep Intra-Individual Variability Questionnaire and self-reported psychosocial, sleep quality, and health outcomes. In Study 2, 100 university students wore actigraphy and completed psychosocial, sleep, and health surveys at multiple timepoints.

RESULTS

In cross-sectional analyses that controlled for mean sleep duration, predisposing/precipitating factors to greater IIV were being an underrepresented racial/ethnic minority (Study 1: F = 13.95, p < .001; Study 2: F = 7.03, p = .009), having greater stress (Study 2: r values ≥ 0.32, p values ≤ .002) or trait vulnerability to stress (Study 1: r values ≥ 0.15, p values < .001), and showing poorer time management (Study 1: r values ≤ -0.12, p values ≤ .004; Study 2: r values ≤ -0.23, p values ≤ .028). In addition, both studies showed that greater sleep IIV was associated with decreased overall sleep quality, independent of mean sleep duration (Study 1: r values ≥ 0.20, p values < .001; Study 2: r values ≥ 0.33, p values ≤ .001). Concordance across subjective and objective IIV measures was modest ( r values = 0.09-0.35) and similar to concordance observed for subjective-objective mean sleep duration measures.

CONCLUSION

Risk for irregular sleep patterns is increased in specific demographic groups and may be precipitated by, or contribute to, higher stress and time management inefficiencies. Irregular sleep may lead to poor sleep quality and adverse health outcomes, independent of mean sleep duration, underscoring the importance of addressing sleep consistency.

Associations of adverse childhood and lifetime experiences with sleep quality and duration among women in midlife

OBJECTIVES

Many women experience sleep problems during midlife. Associations of adverse lifetime experiences-more common among women-with sleep outcomes are understudied.

METHODS

We studied 476 women enrolled in Project Viva 1999-2002. At enrollment, participants reported any lifetime history of abuse and/or financial hardship. At midlife follow-up ∼20 years later, they reported a history of up to 10 adverse childhood experiences (ACEs); 7-day sleep quality (patient-reported outcomes measurement information system sleep disturbance and sleep-related impairment T-scores); and past month average sleep duration. We examined associations of adverse experiences with sleep outcomes, adjusted for childhood sociodemographic variables. We also explored mediation by current depression and anxiety symptoms, hot flash severity, general health, and body mass index.

RESULTS

ACEs were common: 301 women (63%) reported one or more. Each additional ACE was associated with higher midlife sleep disturbance (adjusted β = 0.65 points, 95% confidence interval [CI]: 0.27, 1.02) and sleep-related impairment (0.98, 95% CI: 0.54, 1.41) T-scores, and with sleep duration <6 hour/night (odds ratio 1.19, 95% CI: 1.00, 1.42), but not with continuous sleep duration (-2 minutes, 95% CI: -5, 1). Adverse experiences in adulthood were less consistently associated with sleep quality but were associated with sleep duration, for example, financial hardship during the index pregnancy was associated with 75 minutes (95% CI: -120, -29) shorter sleep duration 2 decades later. Associations of ACEs with sleep disturbance and sleep-related impairment were mediated by midlife depression anxiety and physical health but not by hot flash severity or body mass index.

CONCLUSIONS

Adverse lifetime experiences have deleterious associations with sleep duration and quality in midlife women.

Preliminary exploration of a multidimensional sleep health composite in adolescent females with frequent migraine

OBJECTIVE

This observational study aimed to: (i) describe and explore preliminary psychometric properties of a multidimensional sleep health composite score in adolescent females with frequent migraine; and (ii) examine associations between the composite score, headache characteristics, and emotional health.

BACKGROUND

Sleep health is a multidimensional construct comprised of various dimensions of sleep and circadian functioning, including Regularity, Satisfaction, Alertness, Timing, Efficiency, and Duration (Ru-SATED framework). The Ru-SATED sleep health composite score may provide a holistic perspective of sleep among adolescents with frequent migraine in the context of neurobiological and psychosocial impacts on sleep unique to this developmental period.

METHODS

In all, 60 female adolescents (aged 12-18 years) with high-frequency episodic or chronic migraine completed wrist-worn actigraphy for 10 days and concurrent daily electronic surveys assessing headache, sleep, and emotional health. A sleep health composite score was derived from empirically supported "healthy" versus "unhealthy" ratings on the six Ru-SATED sleep dimensions.

RESULTS

Half of participants (27/54 [50%]) had a composite score ≥4 (i.e., at least four of the six dimensions rated as poor). Convergent validity of the composite score was acceptable (rs = 0.30-0.56, all p < 0.05). Internal consistency among the dimensions was low (α = 0.45). Multivariate multiple regression models indicated that worse sleep health was associated with greater headache-related disability (B = 0.71, p = 0.018) and anxiety (B = 0.59, p = 0.010), and trended toward significance for sadness (B = 0.35, p = 0.052). The composite score was not significantly associated with headache frequency or severity.

CONCLUSIONS

A multidimensional sleep health composite score may provide an alternative, more comprehensive picture of sleep disturbance among adolescent females with frequent migraine. Larger studies are needed to examine psychometric properties more rigorously and further explore the composite score as a potentially unique predictor of headache outcomes.

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.

Sleep timing, sleep regularity, and psychological health in early late life women: Findings from the Study of Women's Health Across the Nation (SWAN)

OBJECTIVES

To examine the associations of actigraphy-assessed sleep timing and regularity with psychological health in early late life women, whose circadian rhythms may be impacted by aging.

DESIGN

Cross-sectional.

PARTICIPANTS

A racially/ethnically diverse sample of 1197 community-dwelling women (mean age 65 years) enrolled in the Study of Women's Health Across the Nation.

MEASURES

Actigraphy-assessed sleep measures included timing (mean midpoint from sleep onset to wake-up) and regularity (standard deviation of midpoint in hours). Psychological health measures included a composite well-being score, the Center for Epidemiological Studies Depression Scale, and the Generalized Anxiety Disorder-7 Scale. Linear and logistic regression models, adjusted for covariates (including sleep duration), tested associations between sleep and psychological health measures.

RESULTS

After covariate adjustment, a sleep midpoint outside of 2:00-4: 00 AM was significantly associated with depressive symptoms (β = 0.88, 95% CI = 0.06, 1.70) and scoring above the cut-point for clinically significant depressive symptoms (OR = 1.72, 95% CI = 1.15, 2.57). Sleep irregularity was significantly associated with lower psychological well-being (β = -0.18, 95% CI = -0.33, -0.03), depressive (β = 1.36, 95% CI = 0.29, 2.44) and anxiety (β = 0.93, 95% CI = 0.40, 1.46) symptoms, and scoring above the cut-point for clinically significant depressive (OR = 1.68, 95% CI = 1.01, 2.79) and anxiety (OR = 1.62, 95% CI = 1.07, 2.43) symptoms.

CONCLUSION

Above and beyond sleep duration, a sleep midpoint outside of 2:00-4:00 AM was associated with depressive symptoms while sleep irregularity was associated with multiple psychological health domains in late life women.

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.

The association between multidimensional sleep health and migraine burden among patients with episodic migraine

STUDY OBJECTIVES

Using the Sleep Regularity, Satisfaction, Alertness, Timing, Satisfaction, and Duration (Ru-SATED) sleep health framework, we examined the association between multidimensional sleep health and headache burden in a cohort of 98 adults with episodic migraine.

METHODS

Participants wore wrist actigraphs and completed twice-daily electronic diaries regarding sleep, headaches, and other health habits for 6 weeks. We calculated separate composite sleep health scores from diary and actigraphy assessed measures using the Ru-SATED framework. We used adjusted multivariable linear regression models to examine the association between composite sleep health scores and headache frequency, duration, and pain intensity.

RESULTS

Among 98 participants (mean age: 35 ± 12 years; 87.8% female), 83 had healthy ranges in ≥ 3 sleep dimensions. In models adjusted for age, sex, menopausal status, physical activity and alcohol intake, good sleep health was associated with fewer headache days/month (actigraphy: 3.1 fewer days; 95% confidence interval: 0.9, 5.7; diary: 4.0 fewer days; 95% confidence interval: 1.1, 6.9). Results did not change substantively with further adjustment for stress and depressive symptoms. We did not observe an association between sleep health and headache duration or intensity, respectively.

CONCLUSIONS

Among patients with episodic migraine, good multidimensional sleep health, but not the majority of singular dimensions of sleep, is associated with approximately 3-4 fewer headache days/month. In addition, there was no association with headache duration or intensity. These findings highlight the importance of assessing multiple dimensions of sleep and suggest that improving sleep health may be a potential clinical strategy to reduce headache frequency.

CITATION

Yoo A, Vgontzas A, Chung J, et al. The association between multidimensional sleep health and migraine burden among patients with episodic migraine. J Clin Sleep Med. 2023;19(2):309-317.

Longitudinal associations between sleep and BMI in a low-income, predominantly Black American sample

OBJECTIVE

Black individuals and those experiencing socioeconomic disadvantage are at increased risk for sleep problems and obesity. This study adds to the limited extant literature examining longitudinal associations between objectively measured sleep and changes in body mass index (BMI) in Black Americans.

DESIGN

We focused on individuals with at least 1 observation of sleep and BMI at 1 of 3 study time points (2013, 2016, and 2018). We modeled longitudinal trends in BMI as a function of time, average of each sleep variable across assessments, and within-person deviations in each sleep variable over time.

SETTING

Data were collected via interviewer-administered at-home surveys and actigraphy in Pittsburgh, PA.

PARTICIPANTS

Our sample comprised 1115 low-income, primarily Black adults, including 862 women and 253 men.

MEASUREMENTS

Sleep measures included actigraphy-measured total sleep time, sleep efficiency, and wakefulness after sleep onset, as well as self-reported sleep quality. We also included objectively measured BMI.

RESULTS

In models adjusted for age, gender, and other sociodemographic covariates (eg, income, marital status), there were no significant longitudinal associations between total sleep time, sleep efficiency, wakefulness after sleep onset, or subjective sleep quality and changes in BMI.

CONCLUSIONS

This study provides further evidence that, among a sample of low-income Black adults, sleep problems are not longitudinally predictive of BMI. Although ample cross-sectional evidence demonstrates that sleep problems and obesity commonly co-occur, longitudinal evidence is mixed. Better understanding the overlap of sleep and obesity over time may contribute to prevention and intervention efforts.

Irregular sleep and cardiometabolic risk: Clinical evidence and mechanisms

Sleep regularity is an essential part of the multidimensional sleep health framework. The phenomenon of irregular sleep patterns is widespread in contemporary lifestyles. This review synthesizes clinical evidence to summarize the measures of sleep regularity and discusses the role of different sleep regularity indicators in developing cardiometabolic diseases (coronary heart disease, hypertension, obesity, and diabetes). Existing literature has proposed several measurements to assess sleep regularity, mainly including the standard deviation (SD) of sleep duration and timing, sleep regularity index (SRI), interdaily stability (IS), and social jetlag (SJL). Evidence on associations between sleep variability and cardiometabolic diseases varies depending on the measure used to characterize variability in sleep. Current studies have identified a robust association between SRI and cardiometabolic diseases. In comparison, the association between other metrics of sleep regularity and cardiometabolic diseases was mixed. Meanwhile, the associations of sleep variability with cardiometabolic diseases differ across the population. SD of sleep characteristics or IS may be more consistently associated with HbA1c in patients with diabetes compared with the general population. The association between SJL and hypertension for patients with diabetes was more accordant than in the general population. Interestingly, the age-stratified association between SJL and metabolic factors was observed in the present studies. Furthermore, the relevant literature was reviewed to generalize the potential mechanisms through which irregular sleep increases cardiometabolic risk, including circadian dysfunction, inflammation, autonomic dysfunction, hypothalamic-pituitary-adrenal (HPA) axis disorder, and gut dysbiosis. Health-related practitioners should give more attention to the role of sleep regularity on human cardiometabolic in the future.

Associations between sleep variability and cardiometabolic health: A systematic review

This review explored the associations between sleep variability and cardiometabolic health. It was performed following PRISMA guidelines. We identified 63 studies. Forty-one studies examined the association between sleep variability and body composition, with 29 examined body mass index (BMI). Thirteen studies used social jet lag (SJL), n = 30,519, with nine reporting a null association. Eight studies used variability in sleep duration (n = 33,029), with five reporting a correlation with BMI. Fourteen studies (n = 133,403) focused on overweight/obesity; significant associations with sleep variability were found in 11 (n = 120,168). Sleep variability was associated with weight gain (seven studies; n = 79,522). Twenty-three studies examined glucose outcomes. The association with hemoglobin A1c (16 studies, n = 11,755) differed depending on populations, while associations with diabetes or glucose were mixed, and none were seen with insulin resistance (five studies; n = 6416). Sixteen studies examined cardiovascular-related outcomes, with inconsistent results. Overall significant associations were found in five studies focusing on metabolic syndrome (n = 7413). In summary, sleep variability was likely associated with obesity, weight gain, and metabolic syndrome. It might be associated with hemoglobin A1c in people with type 1 diabetes. The associations with other outcomes were mixed. This review highlighted the possible association between sleep variability and cardiometabolic health.

The association between social jetlag and parameters of metabolic syndrome and type 2 diabetes: a systematic review and meta‐analysis

This study aims to determine the association between social jetlag and parameters of metabolic syndrome and type 2 diabetes (T2D) in a systematic review and meta-analysis. A systematic literature search was conducted in PubMed/Embase/Scopus until May 2022. Included studies described an association between social jetlag and parameters of the metabolic syndrome and/or T2D, were available full text and written in English or Dutch. Data extraction and quality assessment were performed on pre-piloted forms independently by two reviewers. Results were meta-analysed using random-effects analysis. A total of 6,290 titles/abstracts were screened, 176 papers were read full-text, 68 studies were included. Three studies were rated as low quality, 27 were moderate, and 38 were high quality. High quality studies showed that having social jetlag compared to no social jetlag was significantly associated with higher body mass index in 20 studies (0.49 kg/m² , 95% confidence interval [CI] 0.21-0.77; I²  = 100%), higher waist circumference in seven studies (1.11 cm, 95% CI 0.42-1.80; I²  = 25%), higher systolic blood pressure in 10 studies (0.37 mmHg, 95% CI 0.00-0.74; I²  = 94%) and higher glycated haemoglobin in 12 studies (0.42%, 95% CI 0.12- 0.72; I²  = 100%). No statistically significant associations were found for obesity, abdominal obesity, high- and low-density lipoprotein levels, cholesterol, triglycerides, diastolic blood pressure, hypertension, fasting glucose, homeostatic model assessment for insulin resistance, metabolic syndrome or T2D. Sensitivity analyses did not reduce heterogeneity. Despite substantial heterogeneity, social jetlag is associated with certain parameters of the metabolic syndrome and T2D, but not with prevalent metabolic syndrome or T2D. These findings should be interpreted with caution as the level of evidence is low and mostly based on cross-sectional data. Longitudinal studies are needed to further assess the direction of causality.

Multidimensional Sleep Health Is Associated with Cardiovascular Disease Prevalence and Cardiometabolic Health in US Adults

Individual sleep dimensions have been linked to cardiovascular disease (CVD) risk and cardiometabolic health (CMH), but sleep health is multifaceted. We investigated associations of a multidimensional sleep health (MDSH) score, enabling the assessment of sleep health gradients, with CVD and CMH. Participants were 4555 adults aged ≥20 years from the 2017-2018 National Health and Nutrition Examination Survey. A MDSH score, capturing poor, moderate, and ideal sleep was computed from self-reported sleep duration, sleep regularity, difficulty falling asleep, symptoms of sleep disorders, and daytime sleepiness. Survey-weighted multivariable linear and logistic models examined associations of MDSH with CVD and CMH. Ideal and moderate vs. poor MDSH were related to lower odds of hypertension (62% and 41%), obesity (73% and 56%), and central adiposity (68% and 55%), respectively; a statistically significant linear trend was observed across gradients of MDSH (p-trend < 0.001). Ideal vs. moderate/poor MDSH was associated with 32% and 40% lower odds of prevalent CVD and type 2 diabetes, respectively. More favorable MDSH was associated with lower blood pressure, BMI, waist circumference, and fasting glucose. In sex-stratified analyses, ideal vs. moderate/poor MDSH was associated with lower CVD odds and blood pressure in women only. The MDSH framework may be more than just the sum of its parts and could better capture information regarding CVD risk.

Sleep Health Characteristics among Adults Who Attempted Weight Loss in the Past Year: NHANES 2017-2018

Background: The purpose of this study was to characterize sleep health in adults who attempted weight loss in the prior year. Methods: We analyzed data from the National Health and Nutrition Examination Survey 2017–2018 exam cycle. We included 4837 US adults who did (n = 1919) or did not (n = 2918) attempt weight loss in the past year. Participants self-reported their sleep regularity, satisfaction, sleepiness, timing, and duration, which we defined as “good” based on the prior literature. We characterized sleep health by weight loss attempts status, current BMI and weight change among participants who attempted weight loss. Results: On average, participants reported good sleep health in 3.21 ± 1.14 out of the five sleep domains. A total of 13% of participants had good sleep health in all five domains. The prevalence of sleep regularity (52%) was lowest, and the prevalence of infrequent sleepiness was highest (72%), relative to other sleep domains. In models adjusting for BMI, sleep health was similar in participants who did and did not attempt weight loss. Among adults who attempted weight loss, good sleep health was inversely associated with current BMI and self-reported weight change. Discussion: This study’s findings highlight the importance of considering sleep health when engaging with adults attempting weight loss.

Longitudinal Association Between Depressive Symptoms and Multidimensional Sleep Health: The SWAN Sleep Study

BACKGROUND

Depressive symptoms and sleep disturbances disproportionately affect midlife women. While there may be a bidirectional association, few studies have examined whether depressive symptoms are longitudinally associated with subsequent sleep. Sleep is typically considered unidimensional, despite emerging evidence that multidimensional sleep health provides novel information on the sleep-health link.

PURPOSE

The current study examined whether higher depressive symptoms were longitudinally associated with poorer multidimensional sleep health.

METHOD

Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale across six to nine annual assessments in 302 midlife women from the Study of Women's Health Across the Nation. Six months after their last assessment, actigraphy (mean ± standard deviation = 29.3 ± 6.9 days) and self-report were used to assess sleep health components: efficiency, duration, mid-sleep timing, regularity, alertness, and satisfaction, which were dichotomized and summed to create a composite multidimensional sleep health score. Mixed-effects models were used to evaluate the longitudinal associations between depressive symptoms and multidimensional sleep health, as well as individual sleep health components, adjusting for covariates. Exploratory analyses stratified models by race/ethnicity.

RESULTS

Higher depressive symptoms were associated with subsequent poorer multidimensional sleep health (p < .0.001) and lower alertness (p < .0001) and satisfaction with sleep (p < .0001).

CONCLUSIONS

Our finding that higher average depressive symptoms were associated longitudinally with actigraphy-measured poorer sleep health in midlife women is novel and converges with the larger body of evidence that these two common symptoms are strongly associated. The bidirectional relationship between these two prevalent symptoms needs to be studied in prospective longitudinal studies.

The association between sleep health and weight change during a 12-month behavioral weight loss intervention

BACKGROUND

Prior research on the relationship between sleep and attempted weight loss failed to recognize the multidimensional nature of sleep. We examined the relationship between a composite measure of sleep health and change in weight and body composition among adults in a weight loss intervention.

METHODS

Adults (N = 125) with overweight or obesity (50.3 ± 10.6 years, 91% female, 81% white) participated in a 12-month behavioral weight loss intervention, with assessments of sleep, weight, fat mass, and fat-free mass at baseline, 6 months, and 12 months. Six sleep dimensions (regularity, satisfaction, alertness, timing, efficiency, and duration) were categorized as "good" or "poor" using questionnaires and actigraphy. A composite score was calculated by summing the number of "good" dimensions. Obstructive sleep apnea (OSA) was assessed in a subsample (n = 117), using the apnea-hypopnea index (AHI) to determine OSA severity. Linear mixed modeling was used to examine the relationships between sleep health and outcomes of percent weight, fat mass, or fat-free mass change during the subsequent 6-month interval, adjusting for age, sex, bed partner, and race; an additional model adjusted for AHI.

RESULTS

Mean baseline and 6-month sleep health was 4.5 ± 1.1 and 4.5 ± 1.2, respectively. Mean weight, fat mass, and fat-free mass changes from 0 to 6 months were -9.3 ± 6.1%, -16.9 ± 13.5%, and -3.4 ± 3.4%, respectively, and 0.4 ± 4.8%, -0.3 ± 10.3%, and 0.7 ± 4.1% from 6 to 12 months. Better sleep health was associated with greater subsequent weight loss (P = 0.016) and fat loss (P = 0.006), but not fat-free mass loss (P = 0.232). Following AHI adjustment, the association between sleep health and weight loss was attenuated (P = 0.102) but remained significant with fat loss (P = 0.040). Regularity, satisfaction, timing, and efficiency were each associated with weight and/or fat loss (P ≤ 0.041).

CONCLUSIONS

Better sleep health was associated with greater weight and fat loss, with associations attenuated after accounting for OSA severity. Future studies should explore whether improving sleep health, OSA, or the combination improves weight loss.