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

Association Between Chrononutrition Patterns and Multidimensional Sleep Health

BACKGROUND/OBJECTIVES

Sleep health has been associated with diet quality, but the relationship between chrononutrition patterns and multidimensional sleep health is unclear. This study identifies chrononutrition patterns among U.S. adults and examines their associations with multidimensional sleep health.

METHODS

This cross-sectional analysis used data from the 2017-2020 National Health and Nutrition Examination Survey. Chrononutrition behaviors were assessed using two 24 h dietary recalls. Latent profile analysis was used to identify chrononutrition profiles. Multivariable survey regression models determined the associations between chrononutrition patterns and sleep health dimensions.

RESULTS

The sample included 5228 subjects with a median age of 49 years. Of the sample, 52% of the participants were female, and 65% were White. In adjusted models, each additional hour between wake time and first instance of eating was associated with a 19% increase in the odds of poor timing (sleep midpoint < 2:00 a.m. or >4:00 a.m.; 95% CI: 1.07-1.33) and a 21% increase in poor duration (<7 or >9 h/night; 95% CI: 1.09-1.33). Each additional hour between last eating and bedtime was associated with 9% higher odds of poor duration (95% CI: 1.03-1.16). A one-hour longer eating window was associated with 10% lower odds of poor timing (95% CI: 0.84-0.98). We identified five chrononutrition profiles: Typical Eating (reference), Early Finished Eating, Later Heavy Eating, Extended Window Eating, and Restricted Window Eating. The Later Heavy Eating profile exhibited 96% higher odds of poor timing (95% CI: 1.09-3.51) and the Restricted Window Eating profile had 94% higher odds of poor duration (95% CI: 1.10-3.43).

CONCLUSIONS

These findings highlight the importance of unique chrononutrition patterns in relation to multidimensional sleep health. We provide a framework for future studies to identify personalized chrononutrition interventions and their role in improving sleep health.

Conceptualizing Sleep Satisfaction: A Rapid Review

Good, satisfying, sleep is a key indicator and determinant of health and wellness. However, there is no consensus about how to define and measure good sleep. The present research aimed to define sleep satisfaction through the extant literature and disentangle it from sleep quality, a conceptually similar construct. Systematic review methods were adapted for a rapid review approach. The entire review was completed in eight weeks. Tabulation coding with content analysis was used to identify key categories and synthesize findings. A systematic process for generating construct definitions was followed. Database search yielded 51 eligible studies (N > 218,788), representing diverse adult populations, in 20 countries. Designs varied in rigour. Sleep satisfaction was defined as a personal, introspective, and global judgment about one's feelings of contentment with one's sleep, at a particular point in time. Sleep satisfaction was understood as an indicator of general health, impacted by and varied as a function of one's sleep environment and individual-level characteristics. This rapid review contributes to the literature by providing the first systematically generated definition of sleep satisfaction, with strong implications for measurement, research, and practice.

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.

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 of 1-year weight loss from bariatric surgery and self-reported sleep: a prospective cohort

OBJECTIVE

This study examined the association of weight loss following bariatric surgery with self-reported sleep quality after accounting for other sleep-related factors.

METHODS

Participants were from the Bariatric Experience Long Term (BELONG) study. Participants completed a survey up to 6 months before surgery and approximately 1 year after surgery. The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality. One-year percentage total weight loss (%TWL) was determined from electronic medical records. Covariates included demographics, Charlson Comorbidity Index, geocoded variables to assess neighborhood quality, and physical activity. The authors assessed the association between %TWL at 1 year and PSQI component scores with separate cumulative logit models.

RESULTS

There were 997 participants in the analytic cohort. Participants were 86.2% women, 37.0% Hispanic, and 13.7% Black adults. Mean one-year %TWL was 26.3 (SD 8.7). Each 1% increase in %TWL was associated with a 3% better daytime dysfunction score (odds ratio = 1.03; 95% CI: 1.02-1.05) and a 2% better sleep quality score (odds ratio = 1.02; 95% CI: 1.00-1.03). No significant differences were found for the other PSQI components.

CONCLUSIONS

Weight loss from bariatric surgery was associated with better self-reported sleep at 1 year. For people undergoing bariatric surgery, there may be an added benefit of better sleep.