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\begin{document}

\title{The association between diet quality scores with sleep quality among
employees: A cross-sectional study}



\author[1]{Hossein bavi behbahani}%
\author[1]{Fatemeh Borazjani}%
\author[1]{leila sheikhi}%
\author[1]{Rezvan  Amiri}%
\author[1]{Kambiz Ahmadi Angali}%
\author[2]{Sudabeh  Basak Nejad}%
\author[1]{Mahsa Samadani}%
\affil[1]{Ahvaz Jondishapour University of Medical Sciences}%
\affil[2]{Shahid Chamran University of Ahvaz}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Background: Various studies show that the quality of sleep, in employees
can be effective in improving the quality and performance of their work.
Numerous factors such as nutrition and diet can affect the quality of
sleep of people, especially employees. This study was performed to
investigate the relationship between food quality scores (HEI, DII and
DASH score) and sleep quality in employees. Materials and methods: The
present cross-sectional study was performed on 211 employees with a mean
age of 38.75\selectlanguage{ngerman}±11.31. Nutritional status of individuals was determined
through the Food Frequency Questionnaire (FFQ) and to assess sleep
quality, the Pittsburgh Sleep Quality Index (PSQI)was used which
contains questions about delay, duration, sleep effectiveness, sleep
disorders, sleeping pills and daily dysfunction. The quality of sleep
decreases with increasing Pittsburgh index score. The calculated dietary
quality scores include DASH Dietary Adherence Index, Healthy Nutrition
Index (HEI) and Diet Inflammation Index (DII). Results: The results of
this study after adjusting for confounding factors including age, sex,
daily energy intake and BMI showed a significant positive relationship
between DASH diet score and sleep duration (p \textless{}0.001). There
was a significant negative relationship between HEI score and total
score of Pittsburgh Sleep Quality Index (P = 0.003). Also, HEI score had
a significant positive relationship with sleep duration in the
unmodified and modified models (p \textless{}0.001), and a significant
negative relationship was seen in unadjusted and modified model between
DII score and sleep duration (p \textless{}0.001). Conclusion: DASH and
HEI score had a significant positive relationship with sleep duration
and DII had a significant negative relationship with sleep duration. HEI
also significantly improved sleep quality.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{The association between diet quality scores with sleep quality
among employees: A cross-sectional study}

\textbf{Abstract:}

\textbf{Background:} Various studies show that the quality of sleep, in
employees can be effective in improving the quality and performance of
their work. Numerous factors such as nutrition and diet can affect the
quality of sleep of people, especially employees. This study was
performed to investigate the relationship between food quality scores
(HEI, DII and DASH score) and sleep quality in employee.

\textbf{Materials and methods:} The present cross-sectional study was
performed on 211 employees with a mean age of 38.75\selectlanguage{ngerman}±11.31. Nutritional
status of individuals was determined through the Food Frequency
Questionnaire (FFQ) and to assess sleep quality, the Pittsburgh Sleep
Quality Index (PSQI)was used which contains questions about delay,
duration, sleep effectiveness, sleep disorders, sleeping pills and daily
dysfunction. The quality of sleep decreases with increasing Pittsburgh
index score. The calculated dietary quality scores include DASH Dietary
Adherence Index, Healthy Nutrition Index (HEI) and Diet Inflammation
Index (DII).

\textbf{Results:} The results of this study after adjusting for
confounding factors including age, sex, daily energy intake and BMI
showed a significant positive relationship between DASH diet score and
sleep duration (p \textless{}0.001). There was a significant negative
relationship between HEI score and total score of Pittsburgh Sleep
Quality Index (P = 0.003). Also, HEI score had a significant positive
relationship with sleep duration in the unmodified and modified models
(p \textless{}0.001), and a significant negative relationship was seen
in unadjusted and modified model between DII score and sleep duration (p
\textless{}0.001).

\textbf{Conclusion:} DASH and HEI score had a significant positive
relationship with sleep duration and DII had a significant negative
relationship with sleep duration. HEI also significantly improved sleep
quality.

\textbf{Keywords:} sleep quality, DASH score, HEI index

\textbf{What is already known about this topic?}

Previous studies have examined the relationship between consumption of
foods such as fish or food groups such as vegetables with sleep quality,
and few studies have examined dietary quality indices with sleep
quality. There was also no study examining the relationship between diet
quality and employee sleep quality.

\textbf{What does this article add?}

Our study simultaneously examines the relationship between the three
quality indices of diet, including Healthy Eating Index, Dietary
Inflammatory Index and DASH score with sleep quality in employees.

\textbf{Introduction:}

Human health such as cardio-metabolic and mental health is related to
sleeping habits, suggested by epidemiological evidence(1, 2). Most of
evidence suggest that sleep duration and quality can influence on health
of variety of systems in human body(3). Sleep is also very important for
strengthening memory, improving vision, maintaining body temperature,
maintaining and recovering energy(4), and restoration of the brain
energy metabolism(5). Therefore, sleep disorders may significantly
affect the occupational, physical, cognitive and social performance of
people and impair their quality of life(6).

Hormonal disruption, metabolic impairment and inflammatory process can
play a role in the relationship between sleep and health status(7, 8).

The quality of diet can affect sleep quality and health of individuals
and for this reason, has recently been considered by researchers(9).

Studies have shown that diet plays an important role in causing or
modulating inflammation in the body. Accordingly, it has been shown that
following a Western diet increases inflammation, and following healthy
diets such as the Mediterranean diet can be effective in reducing
inflammation(10-12).

CRP, -glutamyl transferase (GGT), carotenoids, uric acid, vitamin C, and
vitamin D, are several biomarkers of inflammation and antioxidants, that
have been related to sleep quality parameters and duration(13).

Evidence shows that there is an association between diet quality and
inflammation, and a healthy dietary pattern can result in lower CRP
levels(14).

Various diet quality scores have been developed to assess adherence to
desirable priori-defined diets and patterns comprehensively and to
investigate the health effects of these diets(15).

The Dietary Inflammatory Index (DII®) is a literature-derived score that
has been developed to evaluate the inflammatory potential of the diet
and link diet to inflammation. It takes into account six inflammatory
markers (i.e., CRP, IL-1beta, IL-4, IL-6, IL-10, and TNF-alpha). The DII
has proven to be of value for its association with health status in the
general population(16, 17).

Of the other priori-defined dietary patterns, the Dietary Approaches to
Stop Hypertension (DASH) diet, which recommends higher intakes of whole
grains, fruits, vegetables, nuts, seeds, legumes, and low-fat dairy, and
lower intakes of processed meat, sodium, and sweetened beverages, was
originally developed to manage high blood pressure(18). The other score
for evaluating the dietary quality is HEI score. The HEI is designed to
examine the overall quality of diets, its adaptation and compatibility
and coordination with dietary guidelines and food pyramid in 1995. This
index is designed to evaluate diet quality in different societies with
different dietary patterns(19).

Technical-administrative employees at universities mainly perform office
tasks, in some cases involving much responsibility and demanding high
levels of concentration. As a result, this population of workers might
be more exposed to situations which might interfere with the duration of
sleep. Given that the role of nutrition in employees' sleep quality has
not been investigated to date, this study aims to investigate the
relationship between Dietary Inflammatory Score, HEI score and DASH
score, and sleep quality in employees.

\textbf{Materials and methods:}

\textbf{Participants} :

Among 525 employees, 94 were not willing to participate, then 431 filled
the consent form. Therefore, 275 of them had complete diet data, however
after considering total energy intakes within the range of 800--4200
kcal, then 211 of participants was taken into the final analysis.
cluster random sampling from different departments in university.

This descriptive-analytic study was conducted on 211 employees using
cluster random sampling from different departments in Ahvaz Jundishapur
University of Medical Sciences, aged between 18-50 who met the inclusion
criteria. The inclusion criteria were willingness to participate in the
study and age between 18-50 years. The exclusion criteria were
reluctance to participate in the study, history of chronic disease,
following vegan diet, pregnancy and lactation, taking certain
medications.

\textbf{Sample size:}

The sample size was determined based on BMI (mean = 28.9 and SD = 4.5)
as the primary outcome from a study conducted by Muscogiuri et al.(20);

The sample size was calculated using following formula:

N= {[} (z\selectlanguage{greek}\textsubscript{1-α/2}\selectlanguage{english})2 \selectlanguage{ngerman}×
sd\textsuperscript{2}{]}/d\textsuperscript{2} (\selectlanguage{greek}α \selectlanguage{english}= 0.05, confidence
level of 95\% and d = 2\%) as 162 subjects. Considering the withdrawal
rate of 35\%, 218 subjects were recruited.

\textbf{Ethical approval:}

The study protocols were fully explained to the participants. The study
protocols were approved by the ethics committee of AJUMS
(IR.AJUMS.REC.1399.717). Each subject was given an informed consent to
sign.

\textbf{Anthropometric and physical assessment:}

Body weight and height was measured in the participants. Body weight was
measured using a Seca scale, with an accuracy of 100 grams. Height was
measured using a Seca stadiometer with an accuracy of 0.5cm. then BMI
was calculated by dividing body weight by the height square.

\textbf{Dietary assessments:}

The used food intake was obtained from the participants, by trained
dietitians, through a structured interview. To determine typical food
intakes, a valid and reliable 147- item semi quantitative FFQ with
standard servings was used(21). the intake frequency of each food item
was asked on a daily, weekly or monthly basis during the past year, and
converted to the gram. Total energy and nutrient intake were then
calculated using Nutritionist IV software as modified for Iranian foods.

\textbf{DII score:}

For developing the latest DII, the relationship between food components
or parameters and specific inflammatory markers was reviewed in the
literature, published in 2010. Each article was assigned a positive or
negative value, based on the effect of the food parameter on
inflammatory markers (+1 = pro inflammatory relationship, 0= no
significant relationship with inflammatory markers, -1 =
anti-inflammatory relationship). Based on the number of pro-inflammatory
and anti-inflammatory articles, an inflammatory effect score for each
food parameter was calculated. First of all, each participant's dietary
intake was linked to the global dataset, for calculating the DII score.
Then the mean intake for each of the 45 food parameters was provided(12,
15).

\textbf{HEI calculation:}

By summing the sub scores in 13 categories, the total HEI score is
calculated: (the score range in parentheses): total vegetables (0--5),
greens and beans (0--5), total fruit (0--5), whole fruit (0--5), whole
grains (0--10), total dairy (0--10), total protein (0--5), seafood and
plant protein (0--5), fatty acids (0--10), sodium, refined grains
(0--10), and ``empty'' calories from solid fats, alcohol, and added
sugars (0--20). A better score, shows a better dietary quality(22).

\textbf{DASH score calculation:}

DASH score, reward points for high intakes of five food groups, such as
fruits, vegetables, nuts and seeds and legumes, low-fat dairy products
and whole grains, according to quantiles ranking (i.e., participants in
the lowest quintiles receive 1 point, those in the 2nd, 3rd, and 4th
quintiles receive 2, 3, and 4 points respectively, and the highest
quintiles, 5 points). Intakes of sodium, sweetened beverages, red meat
and processed meat had the lowest score (i.e., the lowest quintiles are
assigned 5 points and the highest quintiles, 1 point)(23).

\textbf{Sleep quality measurements:}

To assess the sleep quality, The Pittsburg questionnaire (PSQI) was
used. The Pittsburg questionnaire has 7 scales that include mental
quality of sleep (ration of duration of useful sleep from time spent in
bed), sleep disorders (waking up at night), dose of medicine measures
sleep deprivation and dysfunction. The score of each scale is between
0-3 and the score of 3 in each scale determines the maximum negative.
The overall score of this questionnaire is 0 to 21 and the overall score
of 6 and above indicates the inadequacy of sleep quality.

\textbf{Statistical analysis:}

Distributions of demographic, lifestyle behaviors, and dietary
characteristics were compared across sleep quality score and time in bed
(hr/d). Hence, we used chi-square test for categorical variables and
one-way ANOVA test for continues variables.

The Generalized Additive Models (GAM) procedure was applied to the
dataset of 211 without any missing data by smoothening the effect of
covariates~into crud and adjusted models. Moreover, the adjusted Model
include age, sex and total daily energy intake.

GAM uses a link function to establish a relationship between the mean of
the response variable and a smoothed function of the explanatory
variable(s).

We used SPSS version16 and R version 3.6.2 to perform the analyses. P
values \textless{}0.05 were considered significant. The FFQ analysis and
nutrients were estimated using NUT IV software (Nutritionist IV).

\textbf{Results:}

Table 1. shows the relationship between the baseline information of
participants and the score of Sum sleep. Sum sleep score is divided in
to two groups (\textless{}5 hours and \textgreater{}5 hours). None of
the baseline characteristics had significant difference with sum sleep
score.

Table 2. shows the association between the baseline information of
participants and time in bed. time in bed is divided in to two groups
(\textless{}6 hours and \textgreater{}6 hours). None of the baseline
characteristics had significant difference with time in bed.

Table 3. shows the results and relationship between DASH score and sum
sleep and time in bed. Our results showed that there was a negative
relationship between DASH score and sum sleep, but the relationship was
not significant. The data for DASH score were divided into quartiles,
and the first quartile was considered as reference (anti-inflammatory
quartile). Data were analyzed in two models, the first model was the
non-adjusted model and the second one was adjusted for energy intake,
age, BMI. In none adjusted model, there was not any significant
relationship between any of the quartiles and sum sleep. But, the
p-value for second quartile was 0.06 that showed the relationship could
be strong. Also, the results showed that there is a strong positive
association between DASH score and time in bed, in adjusted and none
adjusted model (P\textless{}0.001).

Table 4 shows the association between HEI score and sum sleep and Time
in bed, in adjusted and non-adjusted model. The negative relationship
between HEI and sum sleep and time in bed, in adjusted and non-adjusted
model was strongly significant (P\textless{}0.001). The data for HEI
score were divided into quartiles, and the first quartile was considered
as reference. There was not any significancy in the quartiles.

The relationship between DII score and time in bed and sum sleep is
presented in Table5. The results showed that there is a strong negative
relation between DII and time in bed and sum sleep, in adjusted and
non-adjusted model (P\textless{}0.001). Like the other scores, DII
scores were divided into quartiles, and the relationship between each
quartile was examined, and no significancy was observed.

\textbf{Discussion:}

In the present study, the relationship between DASH, DII and HEI score
with sum sleep and time in bed was assessed, in the employees.

The results of the present study showed that there is a positive
association between DASH score and time in bed; also, there is a strong
negative association between HEI score and sum sleep, and a strong
positive association between HEI score and time in bed. But we noticed a
positive association between DII score and sum sleep. It means that by
increasing HEI and DASH score, sleep quality increases, but by
increasing DII score, the sleep quality decreases.

There are several studies that have surveyed the relationship between
sleep quality and diet quality.

Similar to our study, Liang et al. In a study of 3941 American adults
found that following a dash diet could effectively improve sleep quality
and sleep duration(24). Also, similar to our study, Mossavar-Rahmani in
a study of 2,140 Spanish adults found that increasing the Healthy Eating
Index effectively increased sleep duration and sleep efficiency(25).
Also, another study conducted on 1500 adults in Spain, showed a lower
variation in sleep duration, and lower sleep quality, in the
individuals, adherent to the Mediterranean diet(26). Another study
conducted by Jaussent et.al found a positive effect of Mediterranean
pattern on specific aspects of sleeping(27). Consumption of fruits,
legumes, nuts and fish is an important factor in the indication of
Healthy Eating Index and following a DASH diet(22). Various studies have
shown that these factors can be effective in improving sleep quality.
Jyv\selectlanguage{ngerman}äkorpi et al. Showed that eating vegetables helps to sleep
better(28). Hakkarainen et al. Also showed that people with insomnia eat
less vegetables(29). Kurotani et al. And Cao Y et al. Showed that
consumption of vegetables, fruits and legumes is associated with better
sleep quality(30, 31). Liang et al. Also found that reduced consumption
of vegetables, nuts, and legumes was associated with reduced sleep
duration(24). Other studies also confirm that consumption of fruits and
vegetables is directly related to sleep duration and sleep quality(26,
32).

In addition, Jyväkorpi et al. And del Brutto et al. Showed that fish
consumption can be effective in improving sleep duration(33). Kurotani
et al. Also showed that fish consumption can reduce the PQS (poor
quality of sleep) score(30) and nutrition showed that fish is related to
sleep timing(34).

Refined grains, Sodium, Added Sugars and Saturated Fats are factors that
reduction of their consumption can increase the score of the Healthy
Eating Index(22). Sodium is also important in determining the score of
dash diet(35). Mossavar-Rahmani et al. showed that among the components
of AHEI-2010, decreased sodium intake was most strongly associated with
higher sleep efficiency (25). Grandne et al. Showed that sugar
consumption was directly related to poor sleep quality(36). Kant et al.
Also showed that consuming Sugars during sleep is also effective(37).
Jyväkorpi et al. Also showed that consumption of Sugars and Saturated
Fats are associated to poor sleep quality(28). Moreover, other animal
and small human experimental studies have shown that high-saturated-fat
diets may influence in circadian rhythms(38, 39). Other studies with
results aligned to our study, showed that higher DII scores are
negatively associated with sleep quality.

In a recent study by Lopes et al, it was shown that the DII score was
only positively associated with daytime sleepiness as a component of
PSQI(40). In the study by Godos et al, participants in the highest
quartile of DII score were more likely to have poor sleep(41).

An interventional study also showed that an anti-inflammatory diet
including increased intake of fruits, vegetables, lean protein, and
reduced intake of added sugars and SFAs could improve sleep quality(42).

There are some possible explanations for the mechanisms underlying the
inverse association between sleep quality and the healthy pattern, one
of the explanations is that proteins include tryptophan that is a
component of serotonin production and neurosecretory hormone melatonin.
sleep cycle, either directly or indirectly, and brain function is
controlled by Serotonin(43).

Generally, serotonin promotes wakefulness, also regulates sleep through
changes in the concentration of melatonin(43, 44). Melatonin exerts a
hypnotic effect through thermoregulatory mechanisms by lowering the core
body temperature, reducing arousal and increasing sleep-propensity(44).

Also, vitamin B6, pyridoxine, is required for the synthesis of serotonin
from tryptophan(45). The 5-Hydroxytryptophan is an intermediate in this
process, and is converted to serotonin by a pyridoxal 5\selectlanguage{english}'-dependent
enzyme(44, 45). In relation, dietary niacin is involved in leaving more
tryptophan to be used for the synthesis of serotonin(46).

In addition, folate is involved in the metabolism of monoamines like
serotonin in the brain(46). The N-3 fatty acids are required to convert
serotonin to melatonin by the enzyme
arylalkylamine-Nacetyltransferase(44).

Other nutrients such as Magnesium enhances the secretion of melatonin
from the pineal gland by stimulating serotonin N-acetyltransferase
activity, which is the key enzyme in melatonin synthesis(47). B12 also
increases melatonin receptors in the brain(48).

In addition, oxidative stress can lead to insomnia(49). Diets with rich
in antioxidant vitamins including vitamin C, have a high score of the
healthy dietary pattern, which may decrease the levels of oxidative
stress and prevent the development of DIS. Therefore, the
above-mentioned nutrients or their combination might have a beneficial
role in initiating sleep.

Increased HEI and DASH scores are associated with increased protein
intake resulting in increased tryptophan. Diets with higher DASH and HEI
score, contain more dark green vegetables, fruits, nuts and seeds(22,
23).

As a result, it increases the intake of magnesium, folate and vitamin C
and other antioxidants from the diet, which are important factors in
regulating effective hormones in sleep(30).

Diets with higher HEI and DASH scores have more variety of foods, and
contain whole grains that are high in B vitamins, so they can help
individuals with their sleep quality.

Increasing fish intake increases omega-3 and b12 fatty acids, which
improve sleep quality(48). Fish also contains glycine, which studies
show can improve sleep satisfaction(50).

Decreasing the DII score of a diet indicates the anti-inflammatory
properties of the diet, which together with increasing the consumption
of nutrients such as magnesium, niacin B6, B12, folate, vitamin C and
omega 3, are effective in regulating melatonin(44).

Other anti-inflammatory agents that have been shown to be effective in
improving quality and sleep time include potassium, fiber, and
calcium(24, 30, 51).

Studies have also shown that some nutrients that have inflammatory
properties in the DII index, play an important role in reducing sleep
duration and sleep quality. These nutrients include total fat, SFA and
cholesterol(52, 53).

One of the reasons that increased DII scores, results in decreased sleep
quality, may be because of the increase in CRP levels, that is related
to increased sleep apnea. Also, researchers have found that inflammation
and inflammatory factors are positively linked to oxidative stress;
these factors include advanced-glycation end products like erythrulose
that decreases sleep quality.

One of the strengths of this study is that no such study has been
conducted in the south of Iran, about dietary scores and sleep quality,
also no study has been done to evaluate the food and nutrition of
employees and that the results of this study provide conditions for
other studies. But there are limitations to this study, too. We could
not cover different ethnicities and our sample size was limited to
Jundishapur University and other university centers in the province were
not surveyed; also, the sample size of the study reduced the original
sample size by excluding very high and low calories.

\textbf{Conclusion:}

The results from this study showed that there is a significant
relationship between dietary health scores, such as HEI and DASH score
and sleep quality derived from PQSI in staff of AJUMS. Also, the results
showed that increasing DII score, which is caused by increasing the
consumption of inflammatory foods, reduces the quality of sleep. From
this article it can be concluded that one of the ways to improve the
quality of sleep can be a change in diet, especially shifting to healthy
eating patterns.

\textbf{Funding/support:} This research was supported by the Research
Deputy of Ahvaz Jundishapur University of Medical Sciences and Social
Determinants of Health Research Center to provide the funding of this
research project (IR.AJUMS.REC. 1399.7.17).

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\textbf{Table 1. Characteristics, diet quality scores and food intakes
according to sum sleep quality score}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Variable & \textless{}5 sum score & [?]5 sum score &
P-value\textsuperscript{1}\tabularnewline
\midrule
\endhead
Weight (kg) & 75(12.17) & 74.91(13.34) & 0.96\tabularnewline
Age (years) & 39.25(6.54) & 39.94(7.52) & 0.57\tabularnewline
Hight(cm) & 157.84(43.84) & 174.46(125.72) & 0.38\tabularnewline
BMI (kg/m2) & 26.08(3.81) & 26.01(3.69) & 0.97\tabularnewline
\textbf{Sex (N) (\%)} & & & 0.47\tabularnewline
Male & 23(22.3) & 80(77.7) &\tabularnewline
female & 21(18.4) & 93(83.7) &\tabularnewline
\textbf{Marital status (N) (\%)} & & & 0.004\tabularnewline
Married & 32(19) & 136(81) &\tabularnewline
Single & 7(16.3) & 36(83.7) &\tabularnewline
Divorced & 4(100) & 0 &\tabularnewline
\textbf{Education status (N) (\%)} & & & 0.14\tabularnewline
\textless{}12 years & 1(6.3) & 15(93.5) &\tabularnewline
\textgreater{}12 years & 43(21.7) & 155(78.3) &\tabularnewline
\textbf{Smoking status (N) (\%)} & & & 0.76\tabularnewline
Yes & 3(23.1) & 10(74.9) &\tabularnewline
No & 40(19.7) & 163(80.3) &\tabularnewline
\textbf{Income status (N) (\%)} & & & 0.93\tabularnewline
strongly satisfied & 3(21.4) & 11(78.6) &\tabularnewline
partially satisfied & 22(21.8) & 79(78.2) &\tabularnewline
strongly unsatisfied & 14(20.3) & 55(79.7) &\tabularnewline
partially unsatisfied & 4(16) & 21(84) &\tabularnewline
DII score & 0.29(2.40) & -0.02(2.10) & 0.41\tabularnewline
DASH score & 20.35(4.07) & 20.65(3.92) & 0.66\tabularnewline
HEI score & 62.83(7.16) & 64.12(8.54) & 0.37\tabularnewline
Protein\% & 14.26(2.47) & 13.94(2.07) & 0.38\tabularnewline
Carbohydrate\% & 62.09(7.23) & 25.11(8.33) & 0.68\tabularnewline
Fat\% & 25.68(7.92) & 25.11(8.33) & 0.68\tabularnewline
Energy (kcal/d) & 2286.63(725.60) & 2493.33(734.91) &
0.06\tabularnewline
\bottomrule
\end{longtable}

\textbf{Abbreviation} : DII; dietary inflammatory index, DASH; Dietary
Approaches to Stop Hypertension, HEI;Healthy Eating Index

\textsuperscript{1} Based on chi-square test for categorical variables
and one-way ANOVA test for continues variables.

Continuous variables are shown as means \selectlanguage{ngerman}± SDs, and categorical variables
are shown as percentages. p \textless{} 0.05 was considered
statistically significance\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
Variable\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
Time in bed \textless{}6 h\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
Time in bed [?]6 h\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
P-value\textsuperscript{1}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Weight (kg)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
74.82(13.18)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
75.50(12.71)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.77\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Age (years)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
39.99(7.20)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
38.86(7.89)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.39\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Hight(cm)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
172.79(124.22)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
163.82(29.39)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.66\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
BMI (kg/m2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
25.93(3.66)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
26.46(3.97)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.44\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sex (N) (\%)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.50\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Male\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
85(82.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
18(17.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
female\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
95(83.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
19(16.7)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Marital status (N) (\%)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.004\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Married\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
146(86.9)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
22(13.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Single\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
31(72.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
12(27.9)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Divorced\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2(50)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2(50)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Education status (N) (\%)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.63\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textless{}12 years\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
14(87.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2(12.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textgreater{}12 years\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
164(82.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
34(17.2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Smoking status (N) (\%)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.55\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
10(76.9)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3(23.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
169(83.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
34(16.7)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Income status (N) (\%)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
strongly satisfied\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9 (64.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5 (35.7)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
partially satisfied\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
84 (83.2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
17 (16.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
strongly unsatisfied\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
60 (87)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9 (13)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
partially unsatisfied\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
20 (80)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5 (20)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
DII score\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.05(2.12)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-0.01(2.37)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.86\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
DASH score\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
20.46(3.96)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
21.21(3.87)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.32\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
HEI score\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
63.65(8.11)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
63.90(8.33)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.87\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Protein\%\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
13.99(2.18)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
14.08(2.04)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.82\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Carbohydrate\%\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
62.92(8.26)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
62.11(7.30)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.58\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fat\%\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
25.59(8.33)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
25.91(7.83)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.58\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Energy (kcal/d)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2496.46(737.98)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2232.43(695.25)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.04\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. Characteristics, diet quality scores and food intakes
according to Time in bed}

\textbf{Abbreviation} : DII; dietary inflammatory index, DASH; Dietary
Approaches to Stop Hypertension, HEI;Healthy Eating Index

\textsuperscript{1} Based on chi-square test for categorical variables
and one-way ANOVA test for continues variables.

Continuous variables are shown as means \selectlanguage{ngerman}± SDs, and categorical variables
are shown as percentages. p \textless{} 0.05 was considered
statistically significance

\textbf{Table 3. The Generalized Additive Models associations between
baseline Healthy Eating Index score, sum sleep and Time in bed.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& Model 0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b}\tabularnewline
\midrule
\endhead
\textbf{Sum sleep} & \selectlanguage{greek}β & SE & t-value & P-value & \selectlanguage{greek}β & SE & t-value &
P-value\tabularnewline
HEI Score & -0.11 & 0.001 & -32.07 & \textless{}0.001 & -0.09 & 0.03 &
-2.98 & \textless{}0.05\tabularnewline
Q2 HEI Score & 0.48 & 0.83 & 0.57 & 0.56 & 7.68 & 8.58 & 0.89 &
0.37\tabularnewline
Q3 HEI Score & -0.11 & 1.21 & -0.09 & 0.92 & 1.76 & 1.23 & 0.14 &
0.88\tabularnewline
Q4 HEI Score & 0.48 & 1.78 & 0.27 & 0.78 & 8.30 & 1.82 & 0.45 &
0.65\tabularnewline
\textbf{Time in bed} & & & & & & & &\tabularnewline
HEI Score & 0.08 & 0.001 & 50.55 & \textless{}0.001 & 0.08 & 0.01 & 5.55
& \textless{}0.001\tabularnewline
Q2 HEI Score & -0.23 & 0.48 & -0.47 & 0.63 & -0.35 & 0.47 & -0.75 &
0.45\tabularnewline
Q3 HEI Score & 0.06 & 0.75 & 0.08 & 0.93 & -0.16 & 0.73 & -0.22 &
0.82\tabularnewline
Q4 HEI Score & 0.37 & 1.00 & 0.37 & 0.70 & 0.11 & 0.98 & 0.11 &
0.90\tabularnewline
\bottomrule
\end{longtable}

a. Model 0, GAM generalized additive model without adjustment; b. Model
I, GAM generalized additive model with adjustment for energy intake,
age, BMI

\textbf{Abbreviation} : HEI;Healthy Eating Index, Q; Quartile,
SE;Standard error

\textbf{Table 4. The Generalized Additive Models associations between
baseline DASH score, sum sleep and Time in bed.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& Model 0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b}\tabularnewline
\midrule
\endhead
\textbf{Sum sleep} & \selectlanguage{greek}β & SE & t-value & P-value & \selectlanguage{greek}β & SE & t-value &
P-value\tabularnewline
DASH Score & -0.05 & 0.06 & -0.80 & 0.42 & -5.59 & 7.03 & -0.79 &
0.42\tabularnewline
Q2 DASH Score & -1.82 & 1.15 & -1.15 & 0.11 & -1.37 & 7.27 & -1.88 &
0.06\tabularnewline
Q3 DASH Score & -2.37 & 1.68 & -1.40 & 0.16 & -7.16 & 8.57 & -1.55 &
0.12\tabularnewline
Q4 DASH Score & -2.11 & 2.29 & -.91 & 0.36 & -7.16 & 7.69 & -0.93 &
0.35\tabularnewline
\textbf{Time in bed} & & & & & & & &\tabularnewline
DASH Score & 0.24 & 0.006 & 37.84 & \textless{}0.001 & 2.05 & 5.703 &
3.61 & \textless{}0.001\tabularnewline
Q2 DASH Score & 0.58 & 0.52 & 1.12 & 0.26 & 0.70 & 0.57 & 1.35 &
0.17\tabularnewline
Q3 DASH Score & 0.23 & 0.75 & 0.30 & 0.76 & 0.15 & 0.75 & 0.21 &
0.83\tabularnewline
Q4 DASH Score & 0.03 & 1.03 & 0.02 & 0.97 & 0.13 & 1.03 & 0.12 &
0.89\tabularnewline
\bottomrule
\end{longtable}

a. Model 0, GAM generalized additive model without adjustment; b. Model
I, GAM generalized additive model with adjustment for energy intake,
age, BMI

\textbf{Abbreviation} : DASH; Dietary Approaches to Stop Hypertension,
Q; Quartile, SE;Standard error

\textbf{Table 5. The Generalized Additive Models associations between
baseline Dietary Inflammatory Index score, sum sleep and Time in bed.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& Model 0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
0\textsuperscript{a} & Model 0\textsuperscript{a} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b} & Model
1\textsuperscript{b} & Model 1\textsuperscript{b}\tabularnewline
\midrule
\endhead
\textbf{Sum sleep} & \selectlanguage{greek}β & SE & t-value & P-value & \selectlanguage{greek}β & SE & t-value &
P-value\tabularnewline
DII Score & -0.10 & 0.12 & -0.79 & 0.42 & -1.02 & 1.30 & -0.78 &
0.43\tabularnewline
Q2 DII Score & 0.02 & 1.37 & 0.01 & 0.98 & 5.04 & 8.22 & 0.61 &
0.54\tabularnewline
Q3 DII Score & 0.10 & 2.15 & 0.05 & 0.96 & -1.393 & 8.02 & -0.24 &
0.80\tabularnewline
Q4 DII Score & 0.82 & 2.79 & 0.29 & 0.76 & -4.90 & 8.03 & -0.61 &
0.54\tabularnewline
\textbf{Time in bed} & & & & & & & &\tabularnewline
DII Score & -0.13 & 0.02 & -5.46 & \textless{}0.001 & -1.35 & 0.23 &
-4.21 & \textless{}0.001\tabularnewline
Q2 DII Score & 0.09 & 0.64 & 0.15 & 0.87 & 1.14 & 6.68 & 0.17 &
0.86\tabularnewline
Q3 DII Score & -0.36 & 1.01 & -0.35 & 0.72 & -5.26 & 1.04 & -0.50 &
0.61\tabularnewline
Q4 DII Score & -0.63 & 1.29 & -0.48 & 0.62 & -7.43 & 1.31 & -0.56 &
0.57\tabularnewline
\bottomrule
\end{longtable}

a. Model 0, GAM generalized additive model without adjustment; b. Model
I, GAM generalized additive model with adjustment for energy intake,
age, BMI

\textbf{Abbreviation} : HEI; Dietary Inflammatory Index, Q; Quartile,
SE;Standard error

\selectlanguage{english}
\FloatBarrier
\end{document}

