Next Article in Journal
Improving Cognitive Function with Nutritional Supplements in Aging: A Comprehensive Narrative Review of Clinical Studies Investigating the Effects of Vitamins, Minerals, Antioxidants, and Other Dietary Supplements
Previous Article in Journal
Maternal Diet Quality during Pregnancy Is Associated with Neonatal Brain White Matter Development
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 24
10.3390/nu15245115
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Berry Consumption and Sleep in the Adult US General Population: Results from the National Health and Nutrition Examination Survey 2005–2018

by
Li Zhang
1,*,
Joshua E. Muscat
1,*,
Penny M. Kris-Etherton
2,
Vernon M. Chinchilli
3,
Julio Fernandez-Mendoza
4,
Laila Al-Shaar
3 and
John P. Richie
1
1
Department of Public Health Sciences, Penn State Cancer Institute, Penn State College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
2
Department of Nutritional Sciences, Pennsylvania State University, University Park, PA 16802, USA
3
Department of Public Health Sciences, Penn State College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
4
Sleep Research & Treatment Center, Department of Psychiatry & Behavioral Health, Penn State Health Milton S. Hershey Medical Center, Penn State University College of Medicine, Hershey, PA 17033, USA
*
Authors to whom correspondence should be addressed.
Nutrients 2023, 15(24), 5115; https://doi.org/10.3390/nu15245115
Submission received: 20 November 2023 / Revised: 11 December 2023 / Accepted: 12 December 2023 / Published: 15 December 2023
(This article belongs to the Section Nutritional Epidemiology)
Review Reports Versions Notes

Abstract

:
Introduction: Poor sleep is associated with numerous adverse health outcomes. Berries are rich in micronutrients and antioxidants that may improve sleep quality and duration. We determined the association of berry consumption and sleep duration and sleep difficulty among adult participants in NHANES. Methods: We analyzed the diet of US adults aged ≥ 20 y using two non-consecutive 24 h recalls from the National Health and Nutrition Examination Survey 2005 to 2018 (N = 29,217). Poor sleep quality was measured by sleep duration (short sleep duration: <7 h), long sleep (≥9 h), and reported sleep difficulty. The relative risk of poor sleep outcomes for berry consumers vs. nonconsumers was modelled using population weight-adjusted multivariable general logistic regression. Results: About 46% of participants reported inadequate sleep duration, and 27% reported sleep difficulties. Twenty-two percent reported consuming berries. Berry consumers had a 10–17% decreased risk of short sleep. The findings were consistent for specific berry types including strawberries and blueberries (p < 0.05). No significant associations with long sleep were found for total berries and any berry types. A decreased risk of sleep difficulties was found to be linked to blackberry consumption (adjusted OR = 0.63, 95% CI: 0.40–0.97; p = 0.036) but not for other berries. Conclusions: US adult berry consumers had a decreased risk of reporting short sleep compared to nonconsumers. Berries are underconsumed foods in the US adult population, and increased berry consumption may improve sleep quality.

1. Introduction

In the United States, about one in three adults does not sleep for the recommended time of 7–9 h nightly [1,2]. Other reports show that 35–50% of adults suffer transient or chronic insomnia [3]. These common sleep complaints can impair quality of life, cause economic burdens, and contribute to physical and mental decline and many chronic diseases [4,5]. Short sleep duration (<7 h), long sleep duration (9 h or longer), and insomnia are all associated with the increased risk/prevalence of cardiovascular diseases [6,7,8,9]. Short (<6 h) and long sleep (≥9 h) durations have been linked to inflammation, metabolic syndrome, obesity, stroke, diabetes, cancer, and mortality [8,10,11,12,13,14,15,16,17]. Short sleepers are more likely to develop hypertension and less likely to report good overall wellbeing [18,19,20], whereas long sleepers have a greater prevalence of underlying mental health concerns: mood disorders, schizophrenia, and depression [21,22].
There has been interest in berries and sleep quality because berries contain melatonin (a natural sleep hormone), antioxidants, essential nutrients (e.g., potassium, vitamin C, calcium, iron, and selenium), and polysaccharides that have been shown to benefit sleep quality [23,24,25,26,27,28]. There have been sleep quality studies in experimental settings; however, the sample sizes have been relatively small [29,30,31]. Furthermore, population studies are limited. One population-based study examined berry consumption and sleep duration. The UK women’s cohort study (UKWCS) of fruits and vegetables, including berries (n = 13,958), used a food frequency questionnaire at baseline and a 4-day food diary during four years of follow-up [32]. Raspberries and strawberries and total polyphenol content of the diet were linearly associated with fewer minutes per day of sleep. These findings were unexpected. Sleep difficulty, a measure of sleep quality, is sometimes termed prolonged sleep latency, a symptom of insomnia [33,34]. The present study was conducted to clarify the relationship of berry consumption with sleep quality using categories of high- and low-risk groups of sleep duration and sleep difficulty.
The National Health and Nutrition Examination Survey (NHANES) provides an opportunity to assess the association of berry intake with sleep risk measures. We used 14 years of data, from 2005–2018, to examine the association between berry (and berry subtype) consumption and the risk of short sleep, long sleep, and sleep difficulty among US non-pregnant, non-lactating adults (age ≥ 20 years). We hypothesized that berry consumption may be inversely associated with short sleep, long sleep, and sleep difficulty (i.e., prolonged sleep latency), respectively.

2. Methods

2.1. Study Design

Initiated in the 1960s, the NHANES, a major program of the National Center for Health Statistics (NCHS), a part of the Centers for Disease Control and Prevention, was designed to assess Americans’ health and nutritional status based on a combination of interviews and physical examinations [35]. Every year starting in 1999, the NHANES recruits about 5000 individuals located in counties across the 50 states to represent the noninstitutionalized US population of all ages. The recruitment strategy was created with a multi-stage probability sampling design covering family, county, state, and region stages. The interviews and physical examinations collected the participants’ demographic, lifestyle, health, and dietary intake information. The NCHS research review board approved NHANES, and data were published in a 2-year cycle. All the participants provided written informed consent. This study was exempt from the Penn State institutional review board approval for using secondary analysis from publicly available data sources.

2.2. Analytic Sample

The analyses were conducted based on combining seven cycles of the NHANES data (2005–2006 to 2017–2018). The analytical sample included adults (20 years or older), excluding pregnant women (n = 554), lactating women (n = 244), and participants who reported missing data on sleep duration or sleep difficulty (n = 144), or who had extreme daily energy intake (n = 1286; <800 or >4200 kcal for males and <500 or >3500 kcal for females). Trained staff collected up to two 24 h food recalls from the participants with the Automated Multiple-Pass Method proposed by the US Department of Agriculture to enhance accuracy [36,37]. The trained interviewers collected the first recall in person, and 3–10 days later, the second food recall was administered by phone. The analyzed sample was restricted to respondents with two 24 h recalls (N = 29,217) (Figure S1).

2.3. Definition of Berry Consumption and Consumers

Berries can be consumed individually and are often a component in mixed or processed foods. For this reason, we developed an algorithm to identify berry intake from the food records. For food items that contained berries as part of a food group (e.g., fruit salad), it was necessary to manually search the Food and Nutrient Database for Dietary Studies (FNDDS) food code description further [38,39]. There are two food code descriptions. We identified berries and berry subtypes such as strawberries, blueberries, cranberries, raspberries, and blackberries as ingredients in food item/recipe using main food code descriptions and the additional FNDDS food code description that provides more additional details on each item. Each of these were from the cycle-specific releases of the FNDDS, which are based on corresponding NHANES cycles; details can be found in previously published methods [40,41]. Berry-flavored alcoholic beverages, typically nutritionally distinct from other berry-containing foods, were excluded.
The amount of berries (berry subtypes) (in grams) in each food recipe/item as identified from the FNDDS was then quantified in cup equivalents using cycle-specific releases of the USDA’s Food Patterns Equivalent Database (FPED) [42]. This database converts all reported foods and beverages in dietary components from NHANES into cup-equivalent units for assessing alignment with the USDA 2015–2020 dietary guidelines for Americans, which are based on cup equivalents [43]. In FPED, there is a category called “citrus, melons, and berries” which was used for conversion.
Berry consumers were identified and classified as respondents who reported >0 cup-equivalent intakes of berry (or berry subtype) fruits in either one or both food recalls.

2.4. Short/Long Sleep Duration and Sleep Difficulty

Based on the answers to the survey question “How much sleep do you usually get at night on weekdays or workdays”? from the sleep disorders questionnaires of NHANES, sleep duration was categorized into three durations: <7 h as short sleep, 7–9 h as sufficient/adequate sleep, and ≥9 h as long sleep. These categories were based on recommended hours of sleep for the adult population by the American Academy of Sleep Medicine and Sleep Research Society and aligned with prior research indicating an association of insufficient sleep (e.g., <7 h) and long sleep (e.g., >9 h) with chronic diseases [44,45]. Sleep difficulty, a symptom of insomnia, was treated as a binary variable and derived from the answers to the question, “Have you ever told a doctor or other health professional that you have trouble sleeping”?

2.5. Covariates

Covariates were selected based on the literature on factors that affect sleep duration and sleep difficulty. A five-level race/ethnicity variable (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, and other), a four-level educational attainment variable (less than high school, high school, some college, 4-year college or more), and a three-level poverty-to-income ratio variable (<1.3, 1.3–1.85, and >1.85) were created to represent the general racial/ethnic spectrum and educational and financial status of the US adult population. Physical activity was treated as a categorical variable with four levels: sedentary (no reported work or leisure physical activity), low (below minimum recommendations), moderate (150–300 min of moderate-intensity or 75–150 min of vigorous activity as recommended), or high (exceeding the level recommended in moderate level). Depression severity in three levels was defined based on PHQ-9 scores: mild (0–3), moderate (4–14), and severe (15–27). The binary variables included sex (Female/Male), and current smoking status (Yes/No). Three-level modified HEI-2015 (a summary diet quality index aligned with dietary guidelines without the berry components) was categorized by quartiles: inadequate: <Q1(43.8), average: Q1–Q3, and optimal: >Q3 (62.5). Body mass index (BMI) was defined as a quotient of weight (kg) and height squared (m2). Age (years), BMI (kg/m2), total energy intake (kcal/day), alcohol consumption (g/day), and caffeine consumption (mg/day, in logarithmic form) were considered continuous variables.

2.6. Statistical Analysis

All analyses were performed with SAS (version 9.4) and reported at a 2-tailed α-level of 0.05 [46]. Appropriate survey sample weights, strata, and primary sampling units were accounted for using survey procedures suggested by NCHS [47]. The comparison of consumers and nonconsumers was based on participants who had provided two dietary recalls, and therefore, the day 2 dietary weights were adjusted for complex study design and non-response, as suggested by NHANES analytical guidelines [48]. A single imputation was performed with the hot-deck technique for missing values of the demographic and lifestyle variables using PROC SURVEYIMPUTE. The Rao-Scott X2 test (a design-modified version of Pearson X2 test) was adopted to compare the categorical demographic and lifestyle characteristics between consumers and nonconsumers. The weight and study-design adjusted t-test was performed to compare continuous variables.
Multivariable logistic regression analysis using the SAS procedure PROC SURVEYLOGISTIC was used to estimate the association between total berry and individual berry consumption and sleep complaints, namely sleep difficulty, short sleep, or long sleep. The models were used to obtain prevalence odds ratios (ORs) and their 95% confidence intervals (CIs).
A backward stepwise selection procedure was implemented in the model to include categorical variables: sex, race/ethnicity, education level, poverty-to-income ratio, physical activity, current smoking status, depression severity, and modified HEI-2015, as well as continuous variables: age (years), BMI, alcohol consumption, total energy, and caffeine intake. All the covariates were selected based on the prior research [32,49,50,51], as well as the selection criteria: a 10% or more in percentage change between measures of association before and after adjusting for a potential confounder in the model [52].

3. Results

3.1. Sample Characteristics by Berry Consumption Status

Study subject characteristics are reported in Table 1. Of the eligible respondents (N = 29,217), approximately 22% of participants (n = 6417) consumed berries (>0 cup equivalents). A higher proportion of berry consumers was women, non-Hispanic White, wealthier, and had a higher level of physical activity, diet quality, and educational attainment. A lower percentage of berry consumers had a history of diabetes or suffered severe depression. Berry consumers also had higher total energy intake, lower intakes of alcohol and caffeine, lower mean BMI, and were less likely to be current smokers than nonconsumers.

3.2. Sample Characteristics by Short Sleep, Long Sleep, and Sleep Difficulty

Table 2 and Table 3 summarize the distribution of short sleep, long sleep, and sleep difficulty according to subject characteristics. Compared to adequate sleepers (54.4%), a lower proportion of short sleepers (21.2%) and long sleepers (24.4%) consumed berries (Table 2). There was no difference in proportion of berry consumers by sleep difficulty status (25.5% vs. 25.4%, Table 3). The distribution of short sleep, long sleep, and sleep difficulty differed by sociodemographic, lifestyle, and dietary factors. A higher proportion of the respondents who reported short sleep or long sleep durations or sleep difficulty earned less income, had less education, reported higher BMI, smoked, and more severe depression symptoms. Compared to the respondents with adequate sleep durations, respondents with short or long sleep durations were more likely to be Non-Hispanic Black and Hispanic/Latino (Mexican American and other Hispanics), and have lower mean alcohol intake, mean energy intake, and lower diet quality. Short sleepers tended to be younger and have higher mean caffeine intake while long sleepers were more likely to be women, older, exercise less, and have lower mean caffeine intake. Similarly, respondents with sleep difficulty were more likely to be non-Hispanic White, older, less physically active, had lower mean intakes of alcohol and energy and higher mean caffeine intake. However, there was no difference in diet quality between respondents with sleep difficulty and those without.

3.3. Berry Consumption Associations with Short but Not with Long Sleep Duration

The models presented in Table 4 compared the odds of short or long sleep duration vs. adequate duration between berry consumers and nonconsumers before and after progressively adjusting for potential confounders. Berry consumers were less likely to report short sleep, an association that was attenuated but remained significant as we controlled for confounders (adjusted OR ranged from 0.75 in the first model to 0.9 in fully adjusted model 3). Consumers of strawberries (adjusted OR ranged from 0.75 to 0.9) and blueberries (OR ranged from 0.69 to 0.83) also had significantly decreased odds of reporting short sleep than nonconsumers. Compared to nonconsumers, consumption of total berries (adjusted OR = 0.8) and blackberries (adjusted OR = 0.44) was associated with lower odds of long sleep when adjusting for sex, race/ethnicity, and age in model 1; however, no significant associations were found for total berries or any berry subtypes in the fully adjusted model 3.

3.4. Berry Consumption Associated with Sleep Difficulty

Table 5 reports the results of the models that compared the odds of sleep difficulty between consumers and nonconsumers before and after adjusting for potential confounders. Compared with nonconsumers, berry consumers (adjusted OR = 0.88, 95% CI: 0.80–0.95, p = 0.003) and those who consumed blueberries, raspberries, or blackberries had significantly lower odds of reporting sleep difficulty compared to nonconsumers, after adjusting for age, sex, and race/ethnicity in model 1. After further adjusting for sociodemographic, lifestyle, and dietary confounders, only blackberry consumption remained significantly associated with a 37% reduced odds of sleep difficulty (p = 0.036).
We did not observe any evidence showing that sex or BMI modified the association between berry consumption and sleep difficulty or short/long sleep, and the addition of melatonin use (n = 369 (1.3%)) as a covariate did not change any of the berry relationships with sleep outcomes.

4. Discussion

In this nationally representative sample of US adults, we observed a disparity in sociodemographic, lifestyle, and dietary factors between berry consumers and nonconsumers, suggesting that berry consumers were more educated, affluent, and health-conscious compared to nonconsumers. Respondents who reported sleep complaints (short sleep, long sleep, or sleep difficulties), compared to the ones without, were more likely to be economically disadvantaged and less educated. Those who reported sleep complaints also tended to be sedentary, smokers, have greater BMI, and more severe depression, exhibiting poorer lifestyle and health awareness. While short or long sleepers had lower diet quality as compared to sleepers with recommended sleep duration, diet quality did not differ by sleep difficulty status. Further, for the first time, we found that consumers of total berries (especially strawberries and blueberries) had significantly decreased odds of reporting short sleep versus nonconsumers, indicating that berry consumption was associated with adequate sleep duration. Additionally, the odds for blackberry consumers to report sleep difficulty decreased by 36% compared to nonconsumers.
Berry consumption was also associated with a healthier lifestyle, a higher socioeconomic status, and a better overall health in Finnish men [53]. However, this study differed from ours because it did not find an association between education attainment and berry consumption. The better diet quality enjoyed by berry consumers as compared to nonconsumers was a new observation, confirming and expanding the previously reported association between higher self-rating for healthfulness of diet and higher berry consumption [41].
Our data also revealed sociodemographic and lifestyle disparities by sleep duration and sleep difficulty, indicating that respondents with recommended sleep duration and without sleep difficulty tend to be economically, physically, and mentally healthier. Our findings were consistent with the local- and national-level cross-sectional studies with adult populations, showing that lower income and education attainment that were highly correlated with lower socioeconomic status were associated with more sleep complaints and less efficient and adequate sleep [54,55,56]. This could be attributable to work conditions, employee-related factors (long work hours, work schedule, and shift work), and waking activities (long TV watching hours) [57,58,59]. We also found that people with sleep complaints, compared with the ones without, tend to have less physical activity, adverse health outcomes, and more severe depression, suggesting that regular physical activity and better physical and mental health could be beneficial to sleep improvement [60,61,62,63].
We also showed that short or long sleepers were more likely to be non-Hispanic Black and Hispanic/Latino while the Non-Hispanic White group were more likely to experience sleep difficulty, confirming the persistent increased prevalence of short sleep durations experienced by non-Hispanic Black and Hispanic/Latino people from a large cross-sectional study of US adults based on the National Health Interview Survey (NHIS) data (2004–2018) [64]. The racial disparity in short sleep could be attributable to night shift and decreased work schedule flexibility that likely prevent non-Hispanic Black and Hispanic/Latino individuals from sleeping enough [65,66]. Depending on the varying definitions of long sleep, the results regarding racial disparity for long sleep were mixed. This prior NHIS study along with the other population-based cross-sectional study found that long sleep (defined as >9 h of sleep duration) was only more prevalent among non-Hispanic Blacks, not Hispanics in the US [23]. The inconsistent results could be due to the inclusion of 9 h sleep duration for long sleep in our study. While various factors could contribute to the observed racial disparity in long sleep, persistently higher multimorbidity prevalence and unemployment rate could help explain the higher prevalence of long sleepers among non-Hispanic Blacks [67,68]. Furthermore, our finding of racial disparity in self-reported sleep difficulty could be due to geographical and environmental influences and reporting bias [69,70].
Another interesting observation in our study was that diet quality differed by sleep duration but not by sleep difficulty status, supporting the findings from two cross-sectional studies (2011–2016) based on the Swedish EpiHealth cohort and the NHANES in which sleepers with inadequate sleep duration were less likely to follow a healthy diet [71,72].
Significantly lower odds of reporting short sleep by consumers of total berries (especially strawberries and blueberries) versus nonconsumers was a novel finding. As a rich source of nutrients and bioactive compounds, berries (especially strawberries and blueberries) may improve sleep quality and sleep duration. Berries are a source of sleep-promoting nutrients such as dietary fiber, folate, potassium, vitamin C, calcium, iron, selenium, and melatonin [23,24,26,73,74,75]. In addition, berries (especially strawberries and blueberries) contain abundant polyphenols (including flavonoids which are specifically high in strawberries and blueberries) with potent antioxidant properties that have been associated with reduced oxidative stress in the CNS, decreased inflammation, as well as improved endothelial function and blood pressure control [76,77,78,79,80,81,82,83,84], all of which are associated with improved sleep regulation, sleep quality, and/or sleep duration [30,31,85,86,87].
Interestingly, an inverse association of sleep difficulty was only observed for blackberry consumption but not other berries. This could be a chance finding. Blackberries contain relatively high levels of vitamin C, magnesium, and iron, which promote quality sleep [24,88,89,90]. However, blackberries are consumed infrequently compared to other berry types. It may be that blackberry consumers differ in a number of health behaviors that may reduce their risk of sleep difficulty.
In interpreting the results of the present study, there are limitations. Using two 24 h self-reported food recalls for determining berry consumer status could misclassify infrequent berry consumers as nonconsumers. This misclassification, if assumed to be nondifferential, may underestimate the associations between berry consumption and inadequate sleep. Secondly, the temporal associations cannot be determined because the study was cross-sectional. Thirdly, NHANES does not contain sleep-related information including shift work and work conditions, which may account for the associations found in this study. Additionally, the sleep duration measurement only included weekday/workday sleep, which could differentiate the study results if the sleep duration was based on weekday and weekend sleep. In addition, sleep data were self-reported, which can be subjective, non-specific, and reflective of underlying sleep disorders or other health concerns. However, substantial evidence supports that subjective and retrospective reporting of sleep measurements, similar to the ones obtained from objective methods, was arguably reliable for capturing sleep habits and reflective of important associations to health outcomes [91,92,93]. Furthermore, self-reported sleep duration was not correlated with sleep difficulty, which is not uncommon for subjective sleep measurements. Thus, we did not consider the association between berry consumption and combination of self-reported sleep duration and sleep difficulty. Finally, we cannot rule out the possibility that residual confounding can explain the associations found in our observational study, although we adjusted for the crucial confounders (demographic, lifestyle, and dietary factors) to mitigate the potential confounding effects. While our results are encouraging, the exact quantity of berries that may benefit sleep and the mechanisms by which berry consumption may affect sleep need further study in a future clinical trial.

5. Conclusions

Berry consumption was associated with decreased odds of short sleep, indicating that berries can, directly or indirectly, promote quality sleep. Because adequate sleep is crucial for the quality of life and disease prevention, berry consumption may benefit health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15245115/s1, Figure S1: Participant flow chart.

Author Contributions

Conceptualization, L.Z., J.E.M., P.M.K.-E., V.M.C., J.F.-M., L.A.-S. and J.P.R.; methodology, L.Z., J.E.M., V.M.C., P.M.K.-E., J.F.-M., L.A.-S. and J.P.R.; software, L.Z.; validation, L.Z. and V.M.C.; formal analysis, L.Z.; investigation, L.Z.; resources, L.Z.; data curation, L.Z.; writing—original draft preparation, L.Z.; writing—review and editing, L.Z., J.E.M., P.M.K.-E., V.M.C., J.F.-M., L.A.-S. and J.P.R.; visualization, L.Z.; supervision, L.Z., J.E.M., P.M.K.-E., V.M.C., J.F.-M., L.A.-S. and J.P.R.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available on the NHANES website, and the analytic codes will be made available pending email request to the corresponding author.

Acknowledgments

We thank Britt Burton-Freeman from the Illinois Institute of Technology, Helen Jensen from Iowa State University, and Valerie Sullivan from Johns Hopkins for their willingness to share their experience with the use of FNDDS.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Liu, Y.; Wheaton, A.G.; Chapman, D.P.; Cunningham, T.J.; Lu, H.; Croft, J.B. Prevalence of Healthy Sleep Duration among Adults—United States, 2014. MMWR Morb. Mortal. Wkly Rep. 2016, 65, 137–141. [Google Scholar] [CrossRef]
  2. Ohayon, M.M. Epidemiology of insomnia: What we know and what we still need to learn. Sleep Med. Rev. 2002, 6, 97–111. [Google Scholar] [CrossRef] [PubMed]
  3. Doghramji, K. The epidemiology and diagnosis of insomnia. Am. J. Manag. Care 2006, 12 (Suppl. 8), S214–S220. [Google Scholar]
  4. Hafner, M.; Stepanek, M.; Taylor, J.; Troxel, W.M.; van Stolk, C. Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis. Rand Health Q. 2017, 6, 11. [Google Scholar]
  5. Rosekind, M.R.; Gregory, K.B. Insomnia risks and costs: Health, safety, and quality of life. Am. J. Manag. Care 2010, 16, 617–626. [Google Scholar] [PubMed]
  6. Cappuccio, F.P.; Cooper, D.; D’Elia, L.; Strazzullo, P.; Miller, M.A. Sleep duration predicts cardiovascular outcomes: A systematic review and meta-analysis of prospective studies. Eur. Heart J. 2011, 32, 1484–1492. [Google Scholar] [CrossRef] [PubMed]
  7. Krittanawong, C.; Tunhasiriwet, A.; Wang, Z.; Zhang, H.; Farrell, A.M.; Chirapongsathorn, S.; Sun, T.; Kitai, T.; Argulian, E. Association between short and long sleep durations and cardiovascular outcomes: A systematic review and meta-analysis. Eur. Heart J. Acute Cardiovasc. Care 2019, 8, 762–770. [Google Scholar] [CrossRef]
  8. Jike, M.; Itani, O.; Watanabe, N.; Buysse, D.J.; Kaneita, Y. Long sleep duration and health outcomes: A systematic review, meta-analysis and meta-regression. Sleep Med. Rev. 2018, 39, 25–36. [Google Scholar] [CrossRef]
  9. Javaheri, S.; Redline, S. Insomnia and Risk of Cardiovascular Disease. Chest 2017, 152, 435–444. [Google Scholar] [CrossRef]
  10. Patel, S.R.; Zhu, X.; Storfer-Isser, A.; Mehra, R.; Jenny, N.S.; Tracy, R.; Redline, S. Sleep duration and biomarkers of inflammation. Sleep 2009, 32, 200–204. [Google Scholar] [CrossRef]
  11. Dowd, J.B.; Goldman, N.; Weinstein, M. Sleep duration, sleep quality, and biomarkers of inflammation in a Taiwanese population. Ann. Epidemiol. 2011, 21, 799–806. [Google Scholar] [CrossRef] [PubMed]
  12. Anothaisintawee, T.; Reutrakul, S.; Van Cauter, E.; Thakkinstian, A. Sleep disturbances compared to traditional risk factors for diabetes development: Systematic review and meta-analysis. Sleep Med. Rev. 2016, 30, 11–24. [Google Scholar] [CrossRef] [PubMed]
  13. Shan, Z.; Ma, H.; Xie, M.; Yan, P.; Guo, Y.; Bao, W.; Rong, Y.; Jackson, C.L.; Hu, F.B.; Liu, L. Sleep duration and risk of type 2 diabetes: A meta-analysis of prospective studies. Diabetes Care 2015, 38, 529–537. [Google Scholar] [CrossRef] [PubMed]
  14. Chen, Y.; Tan, F.; Wei, L.; Li, X.; Lyu, Z.; Feng, X.; Wen, Y.; Guo, L.; He, J.; Dai, M.; et al. Sleep duration and the risk of cancer: A systematic review and meta-analysis including dose-response relationship. BMC Cancer 2018, 18, 1149. [Google Scholar] [CrossRef] [PubMed]
  15. Grandner, M.A.; Hale, L.; Moore, M.; Patel, N.P. Mortality associated with short sleep duration: The evidence, the possible mechanisms, and the future. Sleep Med. Rev. 2010, 14, 191–203. [Google Scholar] [CrossRef]
  16. He, Q.; Sun, H.; Wu, X.; Zhang, P.; Dai, H.; Ai, C.; Shi, J. Sleep duration and risk of stroke: A dose-response meta-analysis of prospective cohort studies. Sleep Med. 2017, 32, 66–74. [Google Scholar] [CrossRef]
  17. Smiley, A.; King, D.; Bidulescu, A. The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 2019, 11, 2582. [Google Scholar] [CrossRef]
  18. Gottlieb, D.J.; Redline, S.; Nieto, F.J.; Baldwin, C.M.; Newman, A.B.; Resnick, H.E.; Punjabi, N.M. Association of usual sleep duration with hypertension: The Sleep Heart Health Study. Sleep 2006, 29, 1009–1014. [Google Scholar] [CrossRef]
  19. Fang, J.; Wheaton, A.G.; Keenan, N.L.; Greenlund, K.J.; Perry, G.S.; Croft, J.B. Association of sleep duration and hypertension among US adults varies by age and sex. Am. J. Hypertens. 2012, 25, 335–341. [Google Scholar] [CrossRef]
  20. Steptoe, A.; Peacey, V.; Wardle, J. Sleep duration and health in young adults. Arch. Intern. Med. 2006, 166, 1689–1692. [Google Scholar] [CrossRef]
  21. Patel, S.R.; Malhotra, A.; Gottlieb, D.J.; White, D.P.; Hu, F.B. Correlates of long sleep duration. Sleep 2006, 29, 881–889. [Google Scholar] [CrossRef]
  22. Zhu, G.; Cassidy, S.; Hiden, H.; Woodman, S.; Trenell, M.; Gunn, D.A.; Catt, M.; Birch-Machin, M.; Anderson, K.N. Exploration of Sleep as a Specific Risk Factor for Poor Metabolic and Mental Health: A UK Biobank Study of 84,404 Participants. Nat. Sci. Sleep 2021, 13, 1903–1912. [Google Scholar] [CrossRef] [PubMed]
  23. Grandner, M.A.; Jackson, N.; Gerstner, J.R.; Knutson, K.L. Dietary nutrients associated with short and long sleep duration. Data from a nationally representative sample. Appetite 2013, 64, 71–80. [Google Scholar] [CrossRef] [PubMed]
  24. Grandner, M.A.; Jackson, N.; Gerstner, J.R.; Knutson, K.L. Sleep symptoms associated with intake of specific dietary nutrients. J. Sleep Res. 2014, 23, 22–34. [Google Scholar] [CrossRef] [PubMed]
  25. Godos, J.; Ferri, R.; Castellano, S.; Angelino, D.; Mena, P.; Del Rio, D.; Caraci, F.; Galvano, F.; Grosso, G. Specific Dietary (Poly)phenols Are Associated with Sleep Quality in a Cohort of Italian Adults. Nutrients 2020, 12, 1226. [Google Scholar] [CrossRef] [PubMed]
  26. Zisapel, N. New perspectives on the role of melatonin in human sleep, circadian rhythms and their regulation. Br. J. Pharmacol. 2018, 175, 3190–3199. [Google Scholar] [CrossRef] [PubMed]
  27. Carpentieri, A.; Díaz de Barboza, G.; Areco, V.; Peralta López, M.; Tolosa de Talamoni, N. New perspectives in melatonin uses. Pharmacol. Res. 2012, 65, 437–444. [Google Scholar] [CrossRef]
  28. Ross, K.; Siow, Y.; Brown, D.; Isaak, C.; Fukumoto, L.; Godfrey, D. Characterization of Water Extractable Crude Polysaccharides from Cherry, Raspberry, and Ginseng Berry Fruits: Chemical Composition and Bioactivity. Int. J. Food Prop. 2015, 18, 670–689. [Google Scholar] [CrossRef]
  29. Garrido, M.; González-Gómez, D.; Lozano, M.; Barriga, C.; Paredes, S.D.; Rodríguez, A.B. A Jerte valley cherry product provides beneficial effects on sleep quality. Influence on aging. J. Nutr. Health Aging 2013, 17, 553–560. [Google Scholar] [CrossRef]
  30. Garrido, M.; Paredes, S.D.; Cubero, J.; Lozano, M.; Toribio-Delgado, A.F.; Muñoz, J.L.; Reiter, R.J.; Barriga, C.; Rodríguez, A.B. Jerte Valley cherry-enriched diets improve nocturnal rest and increase 6-sulfatoxymelatonin and total antioxidant capacity in the urine of middle-aged and elderly humans. J. Gerontol. A Biol. Sci. Med. Sci. 2010, 65, 909–914. [Google Scholar] [CrossRef]
  31. Garrido, M.; Espino, J.; González-Gómez, D.; Lozano, M.; Cubero, J.; Toribio-Delgado, A.F.; Maynar-Mariño, J.I.; Terrón, M.P.; Muñoz, J.L.; Pariente, J.A.; et al. A nutraceutical product based on Jerte Valley cherries improves sleep and augments the antioxidant status in humans. E-Spen Eur. E-J. Clin. Nutr. Metab. 2009, 4. [Google Scholar] [CrossRef]
  32. Noorwali, E.; Hardie, L.; Cade, J. Fruit and Vegetable Consumption and Their Polyphenol Content Are Inversely Associated with Sleep Duration: Prospective Associations from the UK Women’s Cohort Study. Nutrients 2018, 10, 1803. [Google Scholar] [CrossRef]
  33. Schutte-Rodin, S.; Broch, L.; Buysse, D.; Dorsey, C.; Sateia, M. Clinical guideline for the evaluation and management of chronic insomnia in adults. J. Clin. Sleep Med. 2008, 4, 487–504. [Google Scholar] [CrossRef] [PubMed]
  34. Riemann, D.; Benz, F.; Dressle, R.J.; Espie, C.A.; Johann, A.F.; Blanken, T.F.; Leerssen, J.; Wassing, R.; Henry, A.L.; Kyle, S.D.; et al. Insomnia disorder: State of the science and challenges for the future. J. Sleep Res. 2022, 31, e13604. [Google Scholar] [CrossRef] [PubMed]
  35. Centers for Disease Control and Prevention NCfHS. National Health and Nutrition Examination Survey. Available online: https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx (accessed on 10 January 2020).
  36. Moshfegh, A.J.; Rhodes, D.G.; Baer, D.J.; Murayi, T.; Clemens, J.C.; Rumpler, W.V.; Paul, D.R.; Sebastian, R.S.; Kuczynski, K.J.; Ingwersen, L.A.; et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am. J. Clin. Nutr. 2008, 88, 324–332. [Google Scholar] [CrossRef] [PubMed]
  37. Rhodes, D.G.; Murayi, T.; Clemens, J.C.; Baer, D.J.; Sebastian, R.S.; Moshfegh, A.J. The USDA Automated Multiple-Pass Method accurately assesses population sodium intakes. Am. J. Clin. Nutr. 2013, 97, 958–964. [Google Scholar] [CrossRef] [PubMed]
  38. Bodner-Montville, J.; Ahuja, J.K.C.; Ingwersen, L.A.; Haggerty, E.S.; Enns, C.W.; Perloff, B.P. USDA Food and Nutrient Database for Dietary Studies: Released on the web. J. Food Compos. Anal. 2006, 19, S100–S107. [Google Scholar] [CrossRef]
  39. USDA Food and Nutrient Database for Dietary Studies Beltsville, MD2020. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ (accessed on 19 July 2020).
  40. Sullivan, V.K.; Na, M.; Proctor, D.N.; Kris-Etherton, P.M.; Petersen, K.S. Consumption of Dried Fruits Is Associated with Greater Intakes of Underconsumed Nutrients, Higher Total Energy Intakes, and Better Diet Quality in US Adults: A Cross-Sectional Analysis of the National Health and Nutrition Examination Survey, 2007–2016. J. Acad. Nutr. Diet. 2021, 121, 1258–1272. [Google Scholar] [CrossRef]
  41. Burton-Freeman, B.M.; Guenther, P.M.; Oh, M.; Stuart, D.; Jensen, H.H. Assessing the consumption of berries and associated factors in the United States using the National Health and Nutrition Examination Survey (NHANES), 2007–2012. Food Funct. 2018, 9, 1009–1016. [Google Scholar] [CrossRef]
  42. Food Pattern Equivalence Databases, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, 2017–2018. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-databases/ (accessed on 10 January 2021).
  43. DHHS; USDA. 2015–2020 Dietary Guidelines for Americans. 2015. Available online: https://www.dietaryguidelines.gov/sites/default/files/2019-05/2015-2020_Dietary_Guidelines.pdf (accessed on 19 November 2023).
  44. Chaput, J.-P.; Dutil, C.; Featherstone, R.; Ross, R.; Giangregorio, L.; Saunders, T.J.; Janssen, I.; Poitras, V.J.; Kho, M.E.; Ross-White, A.; et al. Sleep duration and health in adults: An overview of systematic reviews. Appl. Physiol. Nutr. Metab. 2020, 45 (Suppl. 2), S218–S231. [Google Scholar] [CrossRef]
  45. Hirshkowitz, M.; Whiton, K.; Albert, S.M.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Katz, E.S.; Kheirandish-Gozal, L.; et al. National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health 2015, 1, 40–43. [Google Scholar] [CrossRef]
  46. SAS. SAS, version 9.4; SAS Institute Inc.: Cary, NC, USA, 2018. [Google Scholar]
  47. Centers for Disease Control and Prevention NCfHS. NHANES Web Tutorial 2019. Available online: https://wwwn.cdc.gov/nchs/nhanes/tutorials/module3.aspx (accessed on 10 February 2020).
  48. Centers for Disease Control and Prevention NCfHS. National Health and Nutrition Examination Survey: Analytic Guidelines, 2011–2016. 2018. Available online: https://wwwn.cdc.gov/nchs/data/nhanes/analyticguidelines/11-16-analytic-guidelines.pdf (accessed on 19 June 2023).
  49. Noorwali, E.A.; Cade, J.E.; Burley, V.J.; Hardie, L.J. The relationship between sleep duration and fruit/vegetable intakes in UK adults: A cross-sectional study from the National Diet and Nutrition Survey. BMJ Open 2018, 8, e020810. [Google Scholar] [CrossRef] [PubMed]
  50. Krittanawong, C.; Kumar, A.; Wang, Z.; Jneid, H.; Baber, U.; Mehran, R.; Tang, W.H.W.; Bhatt, D.L. Sleep Duration and Cardiovascular Health in a Representative Community Population (from NHANES, 2005 to 2016). Am. J. Cardiol. 2020, 127, 149–155. [Google Scholar] [CrossRef] [PubMed]
  51. Deng, M.G.; Nie, J.Q.; Li, Y.Y.; Yu, X.; Zhang, Z.J. Higher HEI-2015 Scores Are Associated with Lower Risk of Sleep Disorder: Results from a Nationally Representative Survey of United States Adults. Nutrients 2022, 14, 873. [Google Scholar] [CrossRef] [PubMed]
  52. Budtz-Jørgensen, E.; Keiding, N.; Grandjean, P.; Weihe, P. Confounder selection in environmental epidemiology: Assessment of health effects of prenatal mercury exposure. Ann. Epidemiol. 2007, 17, 27–35. [Google Scholar] [CrossRef] [PubMed]
  53. Mursu, J.; Virtanen, J.K.; Tuomainen, T.P.; Nurmi, T.; Voutilainen, S. Intake of fruit, berries, and vegetables and risk of type 2 diabetes in Finnish men: The Kuopio Ischaemic Heart Disease Risk Factor Study. Am. J. Clin. Nutr. 2014, 99, 328–333. [Google Scholar] [CrossRef] [PubMed]
  54. Hale, L. Who has time to sleep? J. Public Health 2005, 27, 205–211. [Google Scholar] [CrossRef] [PubMed]
  55. Friedman, E.M.; Love, G.D.; Rosenkranz, M.A.; Urry, H.L.; Davidson, R.J.; Singer, B.H.; Ryff, C.D. Socioeconomic status predicts objective and subjective sleep quality in aging women. Psychosom. Med. 2007, 69, 682–691. [Google Scholar] [CrossRef]
  56. Grandner, M.A.; Patel, N.P.; Gehrman, P.R.; Xie, D.; Sha, D.; Weaver, T.; Gooneratne, N. Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints. Sleep Med. 2010, 11, 470–478. [Google Scholar] [CrossRef]
  57. Stamatakis, K.A.; Kaplan, G.A.; Roberts, R.E. Short sleep duration across income, education, and race/ethnic groups: Population prevalence and growing disparities during 34 years of follow-up. Ann. Epidemiol. 2007, 17, 948–955. [Google Scholar] [CrossRef]
  58. Patel, S.R. Social and demographic factors related to sleep duration. Sleep 2007, 30, 1077–1078. [Google Scholar] [CrossRef] [PubMed]
  59. Basner, M.; Fomberstein, K.M.; Razavi, F.M.; Banks, S.; William, J.H.; Rosa, R.R.; Dinges, D.F. American time use survey: Sleep time and its relationship to waking activities. Sleep 2007, 30, 1085–1095. [Google Scholar] [CrossRef] [PubMed]
  60. Sejbuk, M.; Mirończuk-Chodakowska, I.; Witkowska, A.M. Sleep Quality: A Narrative Review on Nutrition, Stimulants, and Physical Activity as Important Factors. Nutrients 2022, 14, 1912. [Google Scholar] [CrossRef] [PubMed]
  61. Kredlow, M.A.; Capozzoli, M.C.; Hearon, B.A.; Calkins, A.W.; Otto, M.W. The effects of physical activity on sleep: A meta-analytic review. J. Behav. Med. 2015, 38, 427–449. [Google Scholar] [CrossRef] [PubMed]
  62. Andruskiene, J.; Varoneckas, G.; Martinkenas, A.; Grabauskas, V. Factors associated with poor sleep and health-related quality of life. Medicina 2008, 44, 240–246. [Google Scholar] [CrossRef]
  63. Zhai, L.; Zhang, H.; Zhang, D. Sleep duration and depression among adults: A meta-analysis of prospective studies. Depress. Anxiety 2015, 32, 664–670. [Google Scholar] [CrossRef] [PubMed]
  64. Caraballo, C.; Mahajan, S.; Valero-Elizondo, J.; Massey, D.; Lu, Y.; Roy, B.; Riley, C.; Annapureddy, A.R.; Murugiah, K.; Elumn, J.; et al. Evaluation of Temporal Trends in Racial and Ethnic Disparities in Sleep Duration Among US Adults, 2004–2018. JAMA Netw. Open 2022, 5, e226385. [Google Scholar] [CrossRef]
  65. Golden, L. Limited Access: Disparities in Flexible Work Schedules and Work-at-home. J. Fam. Econ. Issues 2008, 29, 86–109. [Google Scholar] [CrossRef]
  66. Bureau of Labor Statistics. Workers on Flexible and Shift Schedules in May 2004. US Dep Labor. 2005. Available online: https://www.bls.gov/news.release/pdf/flex.pdf (accessed on 18 November 2023).
  67. Blanchflower, D.G.; Bryson, A. Unemployment and sleep: Evidence from the United States and Europe. Econ. Hum. Biol. 2021, 43, 101042. [Google Scholar] [CrossRef]
  68. Kalgotra, P.; Sharda, R.; Croff, J.M. Examining multimorbidity differences across racial groups: A network analysis of electronic medical records. Sci. Rep. 2020, 10, 13538. [Google Scholar] [CrossRef]
  69. Grandner, M.A.; Petrov, M.E.; Rattanaumpawan, P.; Jackson, N.; Platt, A.; Patel, N.P. Sleep symptoms, race/ethnicity, and socioeconomic position. J. Clin. Sleep Med. 2013, 9, 897–905. [Google Scholar] [CrossRef] [PubMed]
  70. Grandner, M.A.; Jackson, N.J.; Pigeon, W.R.; Gooneratne, N.S.; Patel, N.P. State and regional prevalence of sleep disturbance and daytime fatigue. J. Clin. Sleep Med. 2012, 8, 77–86. [Google Scholar] [CrossRef] [PubMed]
  71. Jansen, E.C.; Prather, A.; Leung, C.W. Associations between sleep duration and dietary quality: Results from a nationally-representative survey of US adults. Appetite 2020, 153, 104748. [Google Scholar] [CrossRef] [PubMed]
  72. Theorell-Haglöw, J.; Lemming, E.W.; Michaëlsson, K.; Elmståhl, S.; Lind, L.; Lindberg, E. Sleep duration is associated with healthy diet scores and meal patterns: Results from the population-based EpiHealth study. J. Clin. Sleep Med. 2020, 16, 9–18. [Google Scholar] [CrossRef] [PubMed]
  73. Kelly, G.S. Folates: Supplemental forms and therapeutic applications. Altern. Med. Rev. 1998, 3, 208–220. [Google Scholar] [PubMed]
  74. Dashti, H.S.; Scheer, F.A.; Jacques, P.F.; Lamon-Fava, S.; Ordovás, J.M. Short sleep duration and dietary intake: Epidemiologic evidence, mechanisms, and health implications. Adv. Nutr. 2015, 6, 648–659. [Google Scholar] [CrossRef]
  75. Waldhauser, F.; Kovács, J.; Reiter, E. Age-related changes in melatonin levels in humans and its potential consequences for sleep disorders. Exp. Gerontol. 1998, 33, 759–772. [Google Scholar] [CrossRef]
  76. Del Bo, C.; Martini, D.; Porrini, M.; Klimis-Zacas, D.; Riso, P. Berries and oxidative stress markers: An overview of human intervention studies. Food Funct. 2015, 6, 2890–2917. [Google Scholar] [CrossRef]
  77. Del Rio, D.; Borges, G.; Crozier, A. Berry flavonoids and phenolics: Bioavailability and evidence of protective effects. Br. J. Nutr. 2010, 104 (Suppl. 3), S67–S90. [Google Scholar] [CrossRef]
  78. Rendeiro, C.; Rhodes, J.S.; Spencer, J.P. The mechanisms of action of flavonoids in the brain: Direct versus indirect effects. Neurochem. Int. 2015, 89, 126–139. [Google Scholar] [CrossRef]
  79. Martini, D.; Marino, M.; Angelino, D.; Del Bo’, C.; Del Rio, D.; Riso, P.; Porrini, M. Role of berries in vascular function: A systematic review of human intervention studies. Nutr. Rev. 2020, 78, 189–206. [Google Scholar] [CrossRef]
  80. Huang, H.; Chen, G.; Liao, D.; Zhu, Y.; Xue, X. Effects of Berries Consumption on Cardiovascular Risk Factors: A Meta-analysis with Trial Sequential Analysis of Randomized Controlled Trials. Sci. Rep. 2016, 6, 23625. [Google Scholar] [CrossRef]
  81. Luís, Â.; Domingues, F.; Pereira, L. Association between berries intake and cardiovascular diseases risk factors: A systematic review with meta-analysis and trial sequential analysis of randomized controlled trials. Food Funct. 2018, 9, 740–757. [Google Scholar] [CrossRef]
  82. Godos, J.; Vitale, M.; Micek, A.; Ray, S.; Martini, D.; Del Rio, D.; Riccardi, G.; Galvano, F.; Grosso, G. Dietary Polyphenol Intake, Blood Pressure, and Hypertension: A Systematic Review and Meta-Analysis of Observational Studies. Antioxidants 2019, 8, 152. [Google Scholar] [CrossRef]
  83. Kris-Etherton, P.M.; Lefevre, M.; Beecher, G.R.; Gross, M.D.; Keen, C.L.; Etherton, T.D. Bioactive compounds in nutrition and health-research methodologies for establishing biological function: The antioxidant and anti-inflammatory effects of flavonoids on atherosclerosis. Annu. Rev. Nutr. 2004, 24, 511–538. [Google Scholar] [CrossRef]
  84. Paredes-López, O.; Cervantes-Ceja, M.L.; Vigna-Pérez, M.; Hernández-Pérez, T. Berries: Improving human health and healthy aging, and promoting quality life—A review. Plant Foods Hum. Nutr. 2010, 65, 299–308. [Google Scholar] [CrossRef]
  85. Cooper, D.C.; Ziegler, M.G.; Milic, M.S.; Ancoli-Israel, S.; Mills, P.J.; Loredo, J.S.; Von Känel, R.; Dimsdale, J.E. Endothelial function and sleep: Associations of flow-mediated dilation with perceived sleep quality and rapid eye movement (REM) sleep. J. Sleep Res. 2014, 23, 84–93. [Google Scholar] [CrossRef]
  86. Lo, K.; Woo, B.; Wong, M.; Tam, W. Subjective sleep quality, blood pressure, and hypertension: A meta-analysis. J. Clin. Hypertens. 2018, 20, 592–605. [Google Scholar] [CrossRef]
  87. Zielinski, M.R.; Gibbons, A.J. Neuroinflammation, Sleep, and Circadian Rhythms. Front. Cell Infect. Microbiol. 2022, 12, 853096. [Google Scholar] [CrossRef]
  88. Haytowitz, D.B.; Ahuja, J.K.C.; Wu, X.; Somanchi, M.; Nickle, M.; Nguyen, Q.A.; Roseland, J.M.; Williams, J.R.; Patterson, K.Y.; Li, Y.; et al. USDA National Nutrient Database for Standard Reference, Legacy Release. Nutrient Data Laboratory, Beltsville Human Nutrition Research Center, ARS, USDA. 2019. Available online: https://data.nal.usda.gov/dataset/usda-national-nutrient-database-standard-reference-legacy-release (accessed on 10 June 2023).
  89. Matsunaga, T.; Nishikawa, K.; Adachi, T.; Yasuda, K. Associations between dietary consumption and sleep quality in young Japanese males. Sleep Breath 2021, 25, 199–206. [Google Scholar] [CrossRef]
  90. Rondanelli, M.; Opizzi, A.; Monteferrario, F.; Antoniello, N.; Manni, R.; Klersy, C. The effect of melatonin, magnesium, and zinc on primary insomnia in long-term care facility residents in Italy: A double-blind, placebo-controlled clinical trial. J. Am. Geriatr. Soc. 2011, 59, 82–90. [Google Scholar] [CrossRef]
  91. Cappuccio, F.P.; Taggart, F.M.; Kandala, N.-B.; Currie, A.; Peile, E.; Stranges, S.; Miller, M.A. Meta-analysis of short sleep duration and obesity in children and adults. Sleep 2008, 31, 619–626. [Google Scholar] [CrossRef]
  92. Grandner, M.A.; Patel, N.P.; Gehrman, P.R.; Perlis, M.L.; Pack, A.I. Problems associated with short sleep: Bridging the gap between laboratory and epidemiological studies. Sleep Med. Rev. 2010, 14, 239–247. [Google Scholar] [CrossRef]
  93. Knutson, K.L. Sleep duration and cardiometabolic risk: A review of the epidemiologic evidence. Best. Pract. Res. Clin. Endocrinol. Metab. 2010, 24, 731–743. [Google Scholar] [CrossRef]
Table 1. Weighted characteristics of the NHANES respondents by berry consumption status (2005–2018), N = 29,217.
Table 1. Weighted characteristics of the NHANES respondents by berry consumption status (2005–2018), N = 29,217.
CharacteristicsNonconsumers Berry Consumersp-Value
n = 22,800n = 6417
Age, mean ± S.E., y 46.9 ± 0.3 51.1 ± 0.4<0.0001
BMI, mean ± S.E., kg/ m 2 25.6 ± 0.2 24.0 ± 0.3<0.0001
Sex (female), %11,33149.2 (48.2, 50.2)386760.9 (59.2, 62.6)<0.0001
Race/ethnicity, % <0.0001
Non-Hispanic White964364.7 (62.0, 67.5)343876.6 (74.2, 79.1)
Non-Hispanic Black533012.7 (11.1, 14.3)9886.6 (5.6, 7.7)
Mexican American35909.3 (7.9, 10.8)7595.6 (4.6, 6.7)
Other Hispanic21865.6 (4.6, 6.5)5464.3 (3.5, 5.2)
  Other22317.7 (6.9, 8.5)6866.8 (5.7, 7.9)
Poverty-to-income
ratio, %		 <0.0001
<1.3749324.3 (22.8, 25.7)134913.6 (12.3, 15.0)
1.3–1.85881736.4 (35.1, 37.7)229831.6 (29.5, 33.7)
>1.85649039.3 (37.4, 41.3)277054.8 (52.2, 57.3)
Education, % <0.0001
Less than high school586417.0 (15.8, 18.2)8818.6 (7.4, 9.8)
High school556425.6 (24.4, 26.8)118716.3 (14.9, 17.7)
Some college678532.1 (31.0, 33.2)193630.0 (27.8, 32.2)
≥4-year degree458725.3 (23.5, 27.0)241345.1 (42.4, 47.8)
Physical activity, % <0.0001
Sedentary609721.8 (20.7, 22.8)130416.1 (14.6, 17.7)
Low375116.6 (15.8, 17.4)107015.4 (14.3, 16.5)
Moderate319014.5 (13.7, 15.3)109118.1 (16.5, 19.7)
High976247.1 (45.8, 48.4)295250.4 (48.4, 52.4)
Current smoker (Yes), %503722.7 (21.6, 23.7)6429.4 (8.2, 10.6)<0.0001
Depression severity, % <0.0001
0–315,50368.9 (67.9, 69.9)475775.0 (73.4, 76.6)
4–14643827.8 (26.8, 28.7)153723.4 (21.9, 25.0)
15–278593.3 (3.0, 3.7)1231.6 (1.2, 2.0)
Modified HEI-2015, % <0.0001
<43.8613529.1 (27.9, 30.3)81813.0 (11.6, 14.5)
43.8–62.511,71150.8 (49.7, 51.9)298247.8 (45.7, 49.8)
>62.5495420.1 (19.1, 21.2)261739.2 (37.0, 41.5)
Alcohol intake, g/day 9.0 ± 0.3 8.3 ± 0.4<0.0001
Energy intake, kcal/day 2031.9 ± 8.3 2057.2 ± 14.3<0.0001
Caffeine intake, mg/day 167.3 ± 2.9 163.3 ± 3.3<0.0001
All percentages and means ± S.E. were adjusted for survey weights. Comparison between berry consumers and nonconsumers: for categorical data, p-value was estimated using Rao-Scott x2 test for categorical data and t-test for continuous data.
Table 2. Weighted characteristics of the NHANES respondents by sleep duration (2005–2018), N = 29,217.
Table 2. Weighted characteristics of the NHANES respondents by sleep duration (2005–2018), N = 29,217.
CharacteristicsShort Sleep DurationLong Sleep DurationRecommended/Adequate Sleep Durationp-Value
n = 10,159n = 3403n = 15,655
Age, mean ± S.E., y 47.2 ± 0.3 49.8 ± 0.6 48.1 ± 0.3<0.0001
BMI, mean ± S.E., kg/ m 2 29.8 ± 0.129.0 ± 0.228.7 ± 0.1<0.0001
Sex (female), %510848.8 (47.3, 50.3)193560.5 (57.9, 63.2)815552.3 (51.3, 53.4)<0.0001
Race/ethnicity, % <0.0001
Non-Hispanic White386761.3 (58.2, 64.4)159467.0 (63.4, 70.6)744071.5 (69.0, 73.9)
Non-Hispanic Black297016.8 (14.7, 19.0)66310.9 (9.0, 12.9)26858.1 (7.0, 9.2)
Mexican American13808.4 (7.1, 9.7)5169.5 (7.4, 11.6)24538.1 (6.8, 9.4)
Other Hispanic10236.1 (5.0, 7.2)3155.3 (4.0, 6.6)13944.8 (4.0, 5.6)
Other9197.4 (6.5, 8.2)3157.3 (5.6, 8.9)16837.5 (6.6, 8.5)
Poverty-to-income
ratio, %		 <0.0001
<1.3328024.4 (22.6, 26.1)125528.6 (25.9, 31.2)431018.6 (17.4, 19.8)
1.3–1.85388936.6 (35.1, 38.1)134737.8 (35.1, 40.6)590534.4 (32.8, 36.1)
>1.85299039.0 (36.6, 41.3)80133.6 (30.1, 37.1)544046.9 (44.9, 49.0)
Education, % <0.0001
Less than high school242116.2 (14.9, 17.6)93518.9 (17.0, 20.9)338713.3 (12.0, 14.6)
High school251326.6 (25.0, 28.2)84926.4 (24.2, 28.6)338920.7 (19.5, 21.9)
Some college322334.5 (32.8, 36.1)99631.0 (28.3, 33.7)450530.1 (28.7, 31.5)
≥4-year degree200222.7 (20.9, 24.5)62323.7 (20.4, 26.9)437435.9 (33.7, 38.0)
Physical activity, % <0.0001
Sedentary256421.0 (19.6, 22.4)114828.7 (26.2, 31.3)368918.2 (17.1, 19.3)
Low165615.5 (14.6, 16.5)55915.7 (13.9, 17.5)260616.9 (15.8, 17.9)
Moderate134913.8 (12.8, 14.8)45213.8 (11.8, 15.7)248016.7 (15.7, 17.6)
High459049.7 (47.8, 51.5)124441.8 (38.4, 45.2)688048.2 (46.7, 49.7)
Current smoker (Yes), %242525.3 (23.8, 26.7)65720.3 (18.2, 22.4)259515.8 (14.7, 16.9)<0.0001
Depression severity, %
0–3631263.1 (61.5, 64.8)220863.8 (61.4, 66.1)11,77976.2 (74.5, 77.5)
4–14333632.4 (31.0, 33.9)104631.7 (29.2, 34.2)355122.1 (20.8, 23.3)
15–275114.5 (3.8, 5.1)1494.5 (3.4, 5.6)3251.7 (1.4, 2.0)
Modified HEI-2015 <0.0001
<43.8269928.0 (26.6, 29.4)87928.3 (25.2, 31.4)337522.7 (21.3, 24.0)
43.8–62.5513750.2 (48.8, 51.6)168649.6 (47.0, 52.3)787049.9 (48.7, 51.2)
>62.5232321.8 (20.3, 23.3)83822.1 (19.5, 24.7)441027.4 (26.0, 28.7)
Alcohol consumption, g/day 8.3 ± 0.4 8.7 ± 0.6 9.2 ± 0.3<0.0001
Energy intake, kcal/day 2044.7 ± 11.9 1927.7 ± 18.5 2057.3 ± 9.6<0.0001
Caffeine intake, mg/day 176.8 ± 4.0 140.4 ± 4.2 165.7 ± 2.9<0.0001
Berry consumers (Yes), %196221.2 (19.7, 22.6)71924.4 (21.8, 27.0)373628.0 (26.4, 29.6)<0.0001
All percentages and means ± S.E. were adjusted for survey weights. Comparison between berry consumers and nonconsumers: for categorical data, false-discovery rate-adjusted p-value was estimated using Rao-Scott x2 test for categorical data and t-test for continuous data.
Table 3. Weighted characteristics of the NHANES respondents by sleep difficulty (2005–2018), N = 29,217.
Table 3. Weighted characteristics of the NHANES respondents by sleep difficulty (2005–2018), N = 29,217.
CharacteristicsSleep Difficulty (Yes)Sleep Difficulty (No)p-Value
n = 7694n = 21,523
Age, mean ± S.E., y 51.4 ± 0.3 46.6 ± 0.3<0.0001
BMI, mean ± S.E., kg/ m 2 30.2 ± 0.1 28.7 ± 0.1<0.0001
Sex (female), %456159.0 (57.3, 60.8)10,63749.6 (48.6, 50.5)<0.0001
Race/ethnicity, % <0.0001
Non-Hispanic White405174.5 (72.0, 77.0)885065.2 (62.6, 67.8)
Non-Hispanic Black163910.3 (8.9, 11.7)467911.5 (10.1, 12.9)
Mexican American7795.2 (4.2, 6.1)35709.6 (8.1, 11.0)
Other Hispanic6424.1 (3.3, 4.9)20905.7 (4.8, 6.7)
Other5835.9 (5.1, 6.8)23348.0 (7.1, 8.9)
Poverty-to-income ratio, % 0.0196
<1.3253723.3 (21.5, 25.2)630820.9 (19.7, 22.1)
1.3–1.85281734.3 (32.2, 36.3)832436.0 (34.7, 37.4)
>1.85234042.4 (39.8, 45.0)689143.1 (41.2, 45.0)
Education, % <0.0001
Less than high school163613.3 (11.9, 14.7)510715.5 (14.2, 16.7)
High school183324.8 (23.1, 26.6)491822.6 (21.5, 23.7)
Some college253533.9 (32.0, 35.9)618930.7 (29.5, 31.8)
≥4-year degree169027.9 (25.5, 30.4)530931.3 (29.4, 33.1)
Physical activity, % <0.0001
Sedentary609721.8 (20.7, 22.8)521119.1 (18.1, 20.1)
Low375116.6 (15.8, 17.4)349515.9 (15.2, 16.7)
Moderate319014.5 (13.7, 15.3)321115.7 (14.8, 16.5)
High976247.1 (45.8, 48.4)960649.3 (48.0, 50.6)
Current smoker (Yes), %185523.7 (22.0, 25.4)382217.6 (16.7, 18.5)<0.0001
Depression severity, % <0.0001
0–3372951.1 (49.2, 53.0)16,57078.1 (77.1, 79.2)
4–14333142.0 (40.2, 43.8)460220.5 (19.5, 21.5)
15–276346.9 (6.2, 7.6)3511.4 (1.1, 1.6)
Modified HEI-2015 0.518
<43.8198125.6 (23.8, 27.3)497224.8 (23.5, 26.0)
43.8–62.5382050.1 (48.4, 51.8)10,87349.9 (48.8, 51.1)
>62.5189324.3 (22.6, 26.1)567825.3 (24.0, 26.5)
Alcohol intake, g/day 8.7 ± 0.4 8.9 ± 0.2<0.0001
Energy intake, kcal/day 1976.6 ± 12.6 2061.9 ± 8.2<0.0001
Caffeine intake, mg/day 180.5 ± 3.3 160.8 ± 2.8<0.0001
Berry consumers (Yes), %168425.5 (23.7, 27.2)473325.4 (24.1, 26.8)0.941
All percentages and means ± S.E. were adjusted for survey weights. Comparison between berry consumers and nonconsumers: for categorical data, false-discovery rate-adjusted p-value was estimated using Rao-Scott x2 test for categorical data and t-test for continuous data.
Table 4. Adjusted prevalence odds ratio (95% CI) of berry consumption associated with self-reported long or short sleep duration, N = 29,217.
Table 4. Adjusted prevalence odds ratio (95% CI) of berry consumption associated with self-reported long or short sleep duration, N = 29,217.
Types of BerriesCase #
(Consumers/
Nonconsumers)		Self-Reported Sleep Duration Adjusted OR
(95% CI) for Model 1		p-ValueAdjusted OR
(95% CI) for Model 2		p-ValueAdjusted OR
(95% CI) for Model 3		p-Value
Berries
1962/8197Short sleep0.75 (0.68, 0.83) <0.00010.89 (0.80, 0.98) 0.0190.90 (0.81, 0.993) 0.037
719/2684Long sleep0.80 (0.69, 0.93) 0.0040.96 (0.83, 1.12)0.6391.00 (0.86, 1.17)0.97
3736/11,919Normal sleep1.00 1.00 1.00
Strawberries
1214/8945Short sleep0.75 (0.68, 0.83) <0.00010.88 (0.80, 0.98) 0.0140.90 (0.81, 0.99) 0.024
461/2942Long sleep0.87 (0.75, 1.02)0.0921.04 (0.89, 1.22)0.6271.08 (0.92, 1.26)0.351
2326/13,329Normal sleep1.00 1.00 1.00
Blueberries
703/9456Short sleep0.69 (0.60, 0.81) <0.00010.82 (0.70, 0.96) 0.0120.83 (0.71, 0.97) 0.018
296/3107Long sleep0.82 (0.66, 1.01)0.0610.99 (0.80, 1.24)0.9521.03 (0.83, 1.28)0.775
1494/14,161Normal sleep1.00 1.00 1.00
Cranberries
272/9887Short sleep0.70 (0.54, 0.91) 0.0070.84 (0.65, 1.09)0.1820.85 (0.66, 1.10)0.217
102/3301Long sleep0.88 (0.66, 1.19)0.4161.09 (0.80, 1.48)0.5951.13 (0.83, 1.55)0.429
560/15,095Normal sleep1.00 1.00 1.00
Raspberries
105/10,054Short sleep0.69 (0.45, 1.04)0.0750.83 (0.54, 1.27)0.3920.84 (0.55, 1.28)0.416
35/3368Long sleep0.79 (0.48, 1.30)0.3460.98 (0.59, 1.63)0.9231.00 (0.60, 1.66)0.995
222/15,433Normal sleep1.00 1.00 1.00
Blackberries
58/10,101Short sleep0.58 (0.34, 0.99) 0.0450.73 (0.43, 1.26)0.2650.74 (0.43, 1.29)0.277
19/3384Long sleep0.44 (0.21, 0.95) 0.0340.57 (0.26, 1.26)0.1660.58 (0.27, 1.29)0.182
148/15,507Normal sleep1.00 1.00 1.00
Model 1: adjusted for age, sex, and race/ethnicity. Model 2: further adjusted for education level, poverty-to-income ratio, physical activity, current smoking status, depression severity, BMI, alcohol consumption, total energy, and caffeine intake. Model 3: further adjusted for education level, poverty-to-income ratio, physical activity, current smoking status, depression severity, BMI, alcohol consumption, total energy, caffeine intake, and modified HEI-2015.
Table 5. Adjusted prevalence odds ratio (95% CI) of berry consumption associated with self-reported sleep difficulty, N = 29,217.
Table 5. Adjusted prevalence odds ratio (95% CI) of berry consumption associated with self-reported sleep difficulty, N = 29,217.
Types of
Berries		Case #
(Consumers/Nonconsumers)		Sleep
Difficulty		Adjusted OR (95% CI) for Model 1p-ValueAdjusted OR (95% CI)
for Model 2		p-ValueAdjusted OR (95% CI) for Model 3p-Value
Berries1684/6010Yes0.88
(0.80, 0.95) 		0.0030.989
(0.90, 1.09)		0.8140.992
(0.90, 1.09)		0.864
4733/16,790No1.00 1.00 1.00
Strawberries1022/6672Yes0.92
(0.83, 1.03)		0.1281.04
(0.93, 1.17)		0.5011.05
(0.93, 1.18)		0.436
2979/18,544No1.00 1.00 1.00
Blueberries662/7032Yes0.88
(0.77, 0.99) 		0.0450.96
(0.83, 1.10)		0.5350.98
(0.85, 1.13)		0.825
1831/19,692No1.00 1.00 1.00
Cranberries274/7420Yes0.92
(0.73, 1.16)		0.4761.05
(0.83, 1.33)		0.6831.07
(0.85, 1.36)		0.564
660/20,863No1.00 1.00 1.00
Raspberries96/7598Yes0.66
(0.45, 0.97) 		0.0350.75
(0.52, 1.08)		0.1180.74
(0.49, 1.14)		0.169
266/21,257No1.00 1.00 1.00
Blackberries63/7631Yes0.58
(0.37, 0.91) 		0.0180.62
(0.40,0.97)		0.0340.63
(0.40, 0.97) 		0.036
162/21,361No1.00 1.00 1.00
Model 1: adjusted for age, sex, and race/ethnicity. Model 2: further adjusted for education level, poverty-to-income ratio, physical activity, current smoking status, depression severity, BMI, alcohol consumption, total energy, and caffeine intake. Model 3: further adjusted for education level, poverty-to-income ratio, physical activity, current smoking status, depression severity, BMI, alcohol consumption, total energy, caffeine intake, and modified HEI-2015.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, L.; Muscat, J.E.; Kris-Etherton, P.M.; Chinchilli, V.M.; Fernandez-Mendoza, J.; Al-Shaar, L.; Richie, J.P. Berry Consumption and Sleep in the Adult US General Population: Results from the National Health and Nutrition Examination Survey 2005–2018. Nutrients 2023, 15, 5115. https://doi.org/10.3390/nu15245115

AMA Style

Zhang L, Muscat JE, Kris-Etherton PM, Chinchilli VM, Fernandez-Mendoza J, Al-Shaar L, Richie JP. Berry Consumption and Sleep in the Adult US General Population: Results from the National Health and Nutrition Examination Survey 2005–2018. Nutrients. 2023; 15(24):5115. https://doi.org/10.3390/nu15245115

Chicago/Turabian Style

Zhang, Li, Joshua E. Muscat, Penny M. Kris-Etherton, Vernon M. Chinchilli, Julio Fernandez-Mendoza, Laila Al-Shaar, and John P. Richie. 2023. "Berry Consumption and Sleep in the Adult US General Population: Results from the National Health and Nutrition Examination Survey 2005–2018" Nutrients 15, no. 24: 5115. https://doi.org/10.3390/nu15245115

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop