https://doi.org/10.1007/s41105-019-00244-x ORIGINAL ARTICLE
The relationship between sleep duration, sleep quality and dietary intake in adults
Biriz Çakir1 · Fatma Nişancı Kılınç1 · Gizem Özata Uyar2 · Çiler Özenir1 · Emine Merve Ekici1 · Eda Karaismailoğlu3
Received: 17 April 2019 / Accepted: 19 October 2019 / Published online: 6 November 2019
© Japanese Society of Sleep Research 2019
Abstract
To determine the relationship of specific macro- and micro-nutrients and food groups with sleep duration and sleep quality in adults. This cross-sectional descriptive study was conducted on 2446 adults aged between 20 and 64 years in Turkey. The participants’ socio-demographic characteristics, anthropometric measurements, and dietary intake (24-h recall) were taken.
The Pittsburgh Sleep Quality Index was used to assess sleep quality. In the study, 48.9% of the participants were male and 51.1% were female, with an average age of 38.7 ± 12.70 years. Total protein, meat, and processed meat product consumption rates of long sleepers were found to be lower than those of normal sleepers (p < 0.05). Saturated fat intake of short sleepers was higher than that of long sleepers (p < 0.018). Participants with good sleep quality were found to consume higher carbohy- drate, fiber, beta-carotene, vitamin E, thiamine, vitamin B6, total folate, vitamin C, calcium, magnesium, potassium, and iron compared to those with poor sleep quality (p < 0.05). When examined in terms of food groups, fruit consumption was higher in individuals with good sleep quality compared to those with poor sleep quality (p < 0.05). In this study, some macro- and micro-nutrients of the diet were found correlated with sleep duration and quality. Mechanisms mediating the relationship between sleep duration and dietary intake are multi-factorial. Because of the differences in appetite-related hormones, such as leptin and ghrelin, and hedonic factors, future studies will benefit from assessing sleep duration/quality and dietary intake.
Keywords Sleep quality · Sleep duration · Dietary nutrients · Food groups · Anthropometric measurements
Introduction
In recent studies, the importance of sleep health in chronic disease development and management has been emphasized [1, 2]. There is growing evidence that the duration and/or quality of sleep and the development and management of many chronic diseases, such as hypertension, diabetes, coronary heart disease, kidney disease, and obesity, are related [1–3]. Individual and environmental factors as well as reduced sleep quality are reported to be the major fac- tors causing weight gain. A large number of studies indicate insufficient sleep duration and quality as important risk fac- tors for obesity worldwide [4–6]. Various meta-analyses and review studies have reported that there is a negative correla- tion between sleep duration and body weight. However, in a critical review of the epidemiological evidence it is unclear from the available adult epidemiological literature whether either short and/or long sleep is associated with obesity or weight gain [7].
Assessing the association between sleep quality and food intake is important because while dietary factors that affect
* Gizem Özata Uyar gizemozata91@gmail.com Biriz Çakir
birizcakir1@gmail.com Fatma Nişancı Kılınç fatmanisanci67@gmail.com Çiler Özenir
cileraslanalp@gmail.com Emine Merve Ekici mervesakioglu@gmail.com Eda Karaismailoğlu edaozturk82@gmail.com
1 Department of Nutrition and Dietetics, Faculty of Health Science, Kırıkkale University, Kırıkkale, Turkey
2 Department of Nutrition and Dietetics, Faculty of Health Science, Gazi University, Ankara, Turkey
3 Department of Bioistatistics, Faculty of Medicine, Kastamonu University, Kastamonu, Turkey
sleep quality can be easily improved, altering sleep quality autonomously [8]. However, there exist few studies evaluat- ing the relationship between sleep quality and dietary intake [8, 9].
Our primary aim was to determine the relationship of specific macro/micro nutrients and food groups with sleep duration and quality in adults.
Methods
Study populationThis cross-sectional study was conducted between July and September 2017 in 20–64 year-old 3262 adults, who were randomly selected using a multi-stage stratified quota sam- pling method in Turkey.
The study data were collected via a questionnaire includ- ing general socio-demographic characteristics (age, gen- der, income status, education, occupation, smoking, alco- hol), physical activity status, sleep characteristics and 24-h dietary recall collected by the researchers with face-to-face interview method. Volunteers were interviewed face-to-face and a signed written consent form was obtained from each participant.
Individuals who met the following criteria were excluded from the present study: life-threatening illness, neurological disease, diagnosed phsychological disorder or sleep disor- ders, use of medication with known effects on sleep, younger than 20 years and older than 65 years, physical and mental disabilities and refuse to participate in the survey. Partici- pants who did not provide informed consent or did not com- plete the study questionnaire were excluded from the study.
In this study, a total of 3262 participants have avail- able data of sleep duration. We excluded 188 subjects because of missing or implausible information on PSQI and those 2636 subjects reported a daily dietary intake. Of those we excluded 127 subjects because of missing BMI or waist–hip circumference as well as 63 subjects less than 750 kcal (n = 55) or above 4500 kcal (n = 8). Finally, data from 2446 participants were included in this study (the overall participation rate in the study was 75%). The study was approved by Kırıkkale University Social Sciences and Humanities Research Ethics Committee (dated 18.07.2017, approval # 7).
Sleep duration and quality
The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep duration and quality. This questionnaire evalu- ates the individual’s sleep quality for the previous 1 month.
The total PSQI score is between 0 and 21, and the sleep quality is “good” if the total score is ≤ 5 and the sleep quality
is considered “poor” if > 5 as defined by Buysse [10]. Reli- ability and validity of the Turkish version has been verified in terms of its validity and reliability by Ağargün and Cron- bach’s alpha internal consistency coefficient was determined as 0.804 [11].
In the evaluation of the sleep period, the question “How many hours of night sleep did you get in the last month?
(which may differ from the time you spent time in bed)”
was used. There are differences in the literature regarding the classification of sleep duration. In the present study, responses to sleep duration were classified in three groups:
short: ≤ 6 h, normal: > 6–≤ 8 h, and long: > 8 h) according to previous studies [12, 13].
Assesment of dietary intake
Dietary intake was measured using 24-h dietary recalls (24-h DR). Recalls were performed via face-to-face interviews. A 24-h dietary recall (24 h) is a structured interview intended to capture detailed information about all foods and bever- ages. A photographic atlas was used to record the type and portion size of the food and meal. The dietary data analysis was analyzed using BeBİS programme. Nutrition Informa- tion Systems (Beslenme Bilgi Sistemi-BeBiS), which is a food software program in compliance with Turkish food, was used for assessment of nutrients, food, and food groups (BEBİS 2004). [14].
Anthropometric measurements
Anthropometric measurements (body weight, height, waist, and hip circumferences) of the participants were taken in accordance with proper techniques [15]. Height was meas- ured with a 0.1 cm accuracy while having no shoes on, in a standing position, looking straight ahead and putting the shoulders and back of the feet in one direction. Weight was measured using a scale while having no shoes on, wearing minimum clothing, and after excretion.
Body mass index (BMI) was calculated as weight in kilogram divided by the squared height in meters [16]. The BMI values of the participants were grouped as underweight (BMI < 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0 ≤ BMI kg/m2) and obese (BMI ≥ 30.0 kg/m2) [17].
Statistical analysis
All statistical analyses were done using a statistical Package for Social Science (Version 22.0; SPSS Inc., Chicago IL, USA) Pearson Chi-square test was used to compare cate- gorical variables. Continuous variables were compared with parametric tests for normal distribution (Independent sample t-test and ANOVA test). In the case of a group difference,
the tukey’s test was used to determine which group the dif- ference originated from. Statistical significance level was set as p < 0.05.
Results
The mean age and BMI of the participants were 38.7 ± 12.70 years and 26.4 ± 4.93 kg/m2, respectively. There was a significant difference in age, gender, education, socio- economic status (for these parameters p < 0.001), smoking (p = 0.008), alcohol use (p = 0.017), PSQI and sub-scales (p < 0.001, p = 0.008 for use of sleep medication). In terms of sleep quality, there is a difference in gender (p = 0.002), education level (p < 0.001), SES (p < 0.001), alcohol use (p = 0.033), WC (p = 0.043), and WC/HC (p = 0.039), sleep latency (p < 0.001), sleep duration (p < 0.001), sleep distur- bance (p < 0.001), use of sleep medication (p < 0.001) and day-time dysfunction (p < 0.001).
When energy and macro- and micro-nutrient intake of the individuals were compared, it was observed that long sleepers had lower energy-from protein (%) (p = 0.015) com- pared to normal sleepers. Saturated fat and retinol intake of short sleepers was significantly higher (p = 0.018, p = 0.029), and PUFA, vitamin E, and thiamine intake was significantly lower compared to normal sleepers (p = 0.024, p = 0.020, and p = 0.036, respectively). It was found that zinc intake was significantly higher in normal sleepers compared to long sleepers (p = 0.019) (Table 2). Consumption of fiber (p < 0.001), beta-carotene (p = 0.003), thiamine (p = 0.024), vitamin B6 (p = 0.023), total folate (p = 0.002), vitamin C (p = 0.002), calcium (p = 0.037), magnesium (p = 0.037), potassium (p = 0.002), and iron (p = 0.017) were found to be significantly higher in individuals with good sleep quality compared to those with poor sleep quality (Table 2).
Consumption of milk and dairy products, meat and pro- cessed meat products was found to be lower (p = 0.014, p = 0.026) in long sleepers compared to short sleepers when the consumption patterns of food groups were examined.
Fruit consumption was higher in individuals with good sleep quality (193.3 ± 202.01 g/day) compared to those with poor sleep quality (171.4 ± 185.36 g/day) (p = 0.006).
Discussion
To the best of our knowledge, this is the first study involving a large population in which both sleep duration and qual- ity along with the status of individuals’ consumption of food groups as well as specific macro- and micro-nutrient intake are addressed together among Turkey. In the study, some nutrients were found to have a relationship with sleep
duration and quality without any significant differences in BMI, hip circumference and regular physical activity status.
Sleep habits are among the probable factors contribut- ing to obesity. The obesity prevalence has been increasing day-by-day due to the increase of working hours and sleep deprivation. Although there are studies indicating that there is an inverse relationship between the duration of short sleep and BMI [18–20], there are other studies reporting no rela- tionship [21, 22] or a U-shaped relationship [23, 24] between these two variables. In line with previous studies [9, 21, 22], we did not identify associations between sleep duration and BMI (Table 1). On the other hand, there was no difference between the BMIs of individuals in terms of sleep qual- ity, which agrees with the literature [25, 26]. Differences in eating habits, reduced diet quality, irregular eating habits, and increased energy intake may be considered as possible mechanisms in elucidating this inconsistency [13, 27].
In previous studies, despite the methodological differ- ences, it is indicated that there is a relationship between short sleep duration or irregular sleep and unhealthy eating habits [28]. Although there exist studies indicating that there is a relationship between intake of energy and sleep duration [6, 19, 29–33] there also exist studies reporting that there is no relationship between the two, as is the case in the present study [9, 12, 33, 34].
Overall, the sleep duration was associated with energy from protein (%) intake, sleep duration/quality, but not sig- nificant difference in carbohydrate and total fat (Table 2).
The relationship between the intake of macronutrients and duration/quality of sleep in adults has been reported in a number of studies [8, 9, 12, 13, 31, 34, 35] but results are largely inconsistent. Some studies were found where there was significant relationship between sleep duration and intake of carbohydrate [31, 34] and protein [12, 31, 34]. In Bavarian adults, there was no significant difference between sleep duration, sleep quality, and sleep mid-point and carbo- hydrate, protein consumption [9]. As seen in the literature, the relationship between carbohydrate and protein intake and sleep duration is complex. Therefore, there are studies pointing out that not only the amount of carbohydrate but also the type of carbohydrate and the glycemic index may affect sleep quality [8, 36].
In the literature, there is no clear result about the relation- ship between fat intake and sleep duration and quality [9, 12, 13, 29, 37–39]. Our findings were similar with some previous studies suggesting sleep duration or quality was not associated with percentage of energy from fat intake [9, 12] but some studies found relation between fat intake and sleep duration [13, 29, 37–39]. However, when the fatty acids were examined, it was found that saturated fat intake of short sleepers was significantly higher, and their PUFA intake was lower than long sleepers. In another study a positive correlation was found between saturated fat and sleep duration [38]. In a review of
Table 1 Distribution of some main characteristics according to sleep duration categories and sleep quality of the study population (frequency and proportion or mean ± SD) Sleep duration categoriesSleep quality Total (n = 2446)< 6 h (n = 449)6–8 h (n = 780)> 8 h (n = 1217)pEffect SizeGood (≤ 5) (n = 991)Poor (> 5) (n = 1455)pEffect size Age (year)38.7 ± 12.7036.8 ± 12.12a38.7 ± 12.20b,c39.51 ± 13.13c< 0.001*0.07939.0 ± 12.7238.6 ± 12.660.3940.032 Sex n (%) Male1196 (48.9%)213 (47.4%)427 (54.7%)556 (45.7%)< 0.001*0.081523 (52.8%)673 (46.3%)0.002*0.064 Female1250 (51.1%)236 (52.6%)353 (45.3%)661 (54.3%)468 (47.2%)782 (53.7%) Education n (%) < Less than high school815 (33.3%)122 (27.2%)213 (27.3%)480 (39.4%)< 0.001*372 (37.5%)443 (30.4%)< 0.001*0.077 High school graduate780 (31.9%)144 (32.1%)244 (31.3%)392 (32.2%)0.0107307 (31.0%)473 (32.5%) College graduate851 (34.8%)183 (40.7%)323 (41.4%)345 (28.3%)312 (31.5%)539 (37.0%) Socioeconomic status n (%) Low829 (33.9%)125 (27.8%)235 (30.2%)469 (38.5%)< 0.001*0.095365 (36.8%)464 (31.9%)< 0.001*0.074 Medium1129 (46.1%)209 (46.5%)356 (45.6%)564 (46.3%)461 (46.5%)668 (45.9%) High488 (20.0%)115 (25.6%)189 (24.3%)184 (15.1%)165 (16.7%)323 (22.2%) Smoking status n (%) Yes888 (36.3%)177 (39.4%)306 (39.2%)405 (33.3%)0.008*0.063341 (34.4%)547 (37.6%)0.1080.033 No1558 (63.7%)272 (60.6%)474 (60.8%)812 (66.7%)650 (65.6%)908 (62.4%) Alcohol drinking status n (%) Yes203 (8.3%)50 (11.1%)69 (8.8%)84 (6.9%)0.017*0.05868 (6.9%)135 (9.3%)0.033*0.043 No2243 (91.7%)399 (88.9%)711 (91.2%)1113 (93.1%)923 (93.1%)1320 (90.7%) BMI (kg/m2)26.4 ± 4.9325.9 ± 4.8626.5 ± 4.8826.6 ± 4.970.0840.04626.6 ± 4.9226.4 ± 4.930.3700.058 BMI groups n (%) Underweight50 (2.0%)11 (3.1%)15 (1.9%)24 (2.0%)0.1830.04316 (1.6%)34 (2.3%)0.3000.039 Normal weight979 (40.0%)200 (44.3%)295 (37.9%)483 (39.7%)391 (39.5%)588 (40.4%) Overweight921 (37.7%)146 (32.5%)317 (40.6%)459 (37.7%)391 (39.5%)530 (36.5) Obese496 (20.2%)90 (20.1%)153 (19.6%)251 (20.7%)193 (19.4%)303 (20.8%) Regular physical activity status (%) Yes452 (18.5%)91 (20.3%)156 (20.0%)205 (16.8%)0.1160.042201 (20.3%)251 (17.3%)0.0580.038 No1994 (81.5%)358 (79.7%)624 (80.0%)1012 (3.2%)790 (79.7%)1204 (82.7%) Waist circumference (cm)89.0 ± 14.5188.0 ± 14.28a89.9 ± 14.37b89.2 ± 14.51a,b0.0770.04889.6 ± 14.6088.7 ± 14.300.043*0.062 Hip circumference (cm)102.3 ± 21.11101.3 ± 10.13103.7 ± 34.14101.8 ± 11.110.0730.045103.1 ± 30.63101.8 ± 11.030.1330.056 WC/HC0.87 ± 0.100.87 ± 0.100.87 ± 0.100.87 ± 0.100.4610.0380.87 ± 0.100.87 ± 0.100.039*0.100 PSQI6.3 ± 2.28.1 ± 2.74a6.3 ± 1.77b5.5 ± 1.75c< 0.001*0.4464.40 ± 0.837.5 ± 1.91< 0.001*2.105 Subjective sleep quality0.98 ± 0.701.21 ± 0.81a0.94 ± 0.64b0.93 ± 0.68c< 0.0010.1510.56 ± 0.501.27 ± 0.670.025− 1.214 Sleep latency0.94 ± 0.891.18 ± 0.99a0.84 ± 0.87b0.91 ± 0.86c< 0.0010.1280.63 ± 0.731.15 ± 0.94< 0.001− 0.616 Sleep duration––––––0.32 ± 0.580.89 ± 0.85< 0.001− 0.797 Sleep efficiency0.04 ± 0.250.02 ± 0.18a0.01 ± 0.13a,b0.06 ± 0.33b< 0.0010.1000.04 ± 0.280.04 ± 0.240.8240.004
many studies, similar to the results in this study, a significant relationship was reported between short sleep duration and increased SFA intake [35]. Short sleepers may have more ten- dency towards unhealthy food choices and an imbalance in their nutritional pattern may explain the higher SFA and the lower PUFA intake.
Available information on micronutrient intake after sleep restriction is controversial. This study indicated significant relationship between sleep duration and the dietary intake of certain micronutrients and there was significant relation between sleep quality and most of micronutrients (Table 3).
However, short and long sleepers’ vitamin E, B1, and zinc intakes were found lower than normal sleepers, but retinol intake was found higher in short sleepers. In line with this study, Grandner et al. [13] found that B1 and E levels were lower in short sleepers because B vitamins act in the regulation of the release of melatonin, which affects sleep [28]. When the micronutrient intake was evaluated in terms of sleep quality beta-carotene, vitamin E, total folic acid, vitamin C, potas- sium, and iron intake were found to be significantly higher in the subjects with good sleep quality than those with poor sleep quality. In this study, high levels of fruit consumption in individuals with good sleep quality may account for higher beta-carotene, vitamin C, total folate, and potassium levels because whole grains, vegetables, and fruit are rich in fiber [40]. Also, vegetables and fruits are rich in vitamin C and potassium [41]. There are studies in the literature focusing on the relationship between sleep quality and macronutrient intake and food groups [8, 9], but there exists no study evaluat- ing the relationship between sleep quality and micronutrient intake. Peuhkuri et al. [42] also emphasized that the relation- ship between nutrient intake and sleep duration and quality is unclear in the literature. The mechanisms by which the effects of micronutrients on sleep are not clear [27]. For this reason, it is considered that this study will make a significant contribu- tion to the literature.
As a result, possibly there are various mechanisms that stimulate the effects of nutrients during sleep. In this study, the complex relationship between dietary intake and sleep was assessed. Relationships between some macro- and micro- nutrients of the diet and sleep duration and quality were found.
Although there seem to be minor differences in vitamin- and mineral content in terms of sleep duration and quality, these small quantities are important in protecting against diseases.
It is difficult to say that consumed foods directly affect sleep because it is unclear via what mechanisms nutrients and nutri- tional elements regulate sleep. Development of various nutri- tion strategies may be an important solution for the prevention of sleep disorders. For this reason, considering all these fac- tors, providing adequate and balanced nutrition training to the individuals by nutritionists will contribute to the improvement of sleep quality.
Data are presented as mean ± standard deviation or number (%) BMI Body mass index, PSQI Pittersburg Sleep Quality Index, WC/HC waist circumference/hip circumference *p < 0.05 a,b,c There is a statistical difference between the different character Table 1 (continued) Sleep duration categoriesSleep quality Total (n = 2446)< 6 h (n = 449)6–8 h (n = 780)> 8 h (n = 1217)pEffect SizeGood (≤ 5) (n = 991)Poor (> 5) (n = 1455)pEffect size Sleep disturbance0.98 ± 0.561.07 ± 0.58a0.92 ± 0.57b0.98 ± 0.56c< 0.0010.0890.67 ± 0.511.19 ± 0.51< 0.001− 1.026 Use of sleep medication0.08 ± 0.400.12 ± 0.51a 0.05 ± 0.30b 0.09 ± 0.41a,b 0.0080.0610.00 ± 0.050.14 ± 0.51< 0.001− 0.369 Daytime dysfunction0.06 ± 0.790.79 ± 0.89a0.58 ± 0.76b0.58 ± 0.76c< 0.0010.1040.10 ± 0.320.97 ± 0.82< 0.001− 1.399
Table 2 Comparison of mean energy, macro- and micro-nutrient intake/day according to sleep duration categories and sleep quality of the study population Data are presented as mean ± standard deviation SFA Saturated fatty acids, MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids *p < 0.05 a, b, c There is a statistical difference between the different character Total (n = 2446)Sleep duration categories(hours/day)Sleep quality < 6 h (n = 449)6–8 h (n = 780)> 8 h (n = 1217)pEffect sizeGood (≤ 5) (n = 991)Poor (> 5) (n = 1455)pEffect size Energy (kcal/day)1760.6 ± 581.651744.9 ± 589.891792.7 ± 606.841745.8 ± 561.400.1750.0371784.4 ± 594.421744.5 ± 572.440.0960.068 Carbohydrates (%)47.5 ± 10.0946.6 ± 10.1047.6 ± 10.2347.8 ± 9.990.1380.04447.8 ± 9.9747.3 ± 10.270.2230.049 Fiber (g)21.8 ± 9.8821.0 ± 9.6322.0 ± 9.7222.0 ± 10.070.1530.03822.6 ± 10.321.3 ± 9.58< 0.001*0.131 Protein (%)15.4 ± 4.2015.6 ± 4.18a,b15.6 ± 4.35a15.1 ± 4.10b0.015*0.05915.3 ± 4.1615.5 ± 4.220.237− 0.050 Fat (%)37.1 ± 9.4537.6 ± 9.6836.9 ± 9.3037.0 ± 9.460.3840.02536.9 ± 9.3137.2 ± 9.550.484− 0.032 SFA (g)24.3 ± 11.2625.5 ± 12.63a24.4 ± 11.21a,b23.8 ± 10.72b0.018*0.05424.2 ± 11.2224.4 ± 11.290.663− 0.018 MUFA (g)24.89 ± 11.5525.4 ± 12.8325.2 ± 11.8124.5 ± 10.860.2390.03325.0 ± 11.4324.8 ± 11.630.7480.017 PUFA (g)18.3 ± 10.4917.2 ± 9.25a18.9 ± 11.22b18.3 ± 10.42a,b0.024*0.05618.7 ± 10.8918.0 ± 10.210.1210.066 Vitamin A (mcg)930.9 ± 830.47945.8 ± 730.41953.7 ± 871.30910.8 ± 838.50.4870.024960.9 ± 851.35910.4 ± 815.560.1410.061 Retinol (mcg)352.1 ± 306.51377.2 ± 328.94a362.3 ± 417.17a,b336.2 ± 192.95b0.029*0.051344.1 ± 194.99357.46 ± 363.510.292− 0.046 Beta-carotene (mcg)2.4 ± 2.682.4 ± 2.392.4 ± 2.412.4 ± 2.910.91302.6 ± 3.002.3 ± 2.410.003*0.110 Vitamin E (mg)17.5 ± 10.0616.5 ± 9.15a 18.2 ± 10.65b 17.5 ± 9.97a,b 0.020*0.06018.0 ± 10.4917.2 ± 9.750.0560.079 Thiamin (mg)0.78 ± 0.360.8 ± 0.3a0.8 ± 0.4b0.8 ± 0.4a,b0.036*0.0130.8 ± 0.360.8 ± 0.340.024*0.029 Riboflavin (mg)1.2 ± 0.671.2 ± 0.591.3 ± 0.731.2 ± 0.650.0820.0711.2 ± 0.671.2 ± 0.660.4260.015 Niacin (mg)22.2 ± 11.8922.2 ± 10.3822.9 ± 11.9921.7 ± 12.330.0970.04622.3 ± 11.6822.2 ± 12.030.8000.008 Vitamin B6 (mg)1.1 ± 0.491.1 ± 0.441.2 ± 0.511.1 ± 0.500.0670.0971.2 ± 0.501.1 ± 0.490.023*0.202 Vitamin B12 (mg)4.1 ± 10.34.1 ± 8.204.6 ± 11.853.7 ± 9.890.1990.0393.9 ± 10.544.1 ± 10.120.633− 0.019 Total folate (mcg)291.3 ± 118.12284.1 ± 108.99296.79 ± 116.15290.46 ± 122.470.1800.036300.2 ± 120.65285.3 ± 116.030.002*0.126 Vitamin C (mg)97.5 ± 82.7292.1 ± 83.4199.8 ± 81.6998.1 ± 83.100.2730.033103.9 ± 86.0293.2 ± 80.130.002*0.129 Calcium (mg)626.5 ± 277.08632.9 ± 280.8639.4 ± 285.03616.3 ± 270.280.1730.038640.74 ± 280.02616.9 ± 274.740.037*0.086 Magnesium (mg)244.7 ± 102.6242.0 ± 99.14248.4 ± 102.2243.4 ± 104.10.4750.024249.9 ± 105.27241.2 ± 100.640.037*0.084 Phosphorus (mg)1031.6 ± 376.01030.8 ± 367.21053.1 ± 378.721018.1 ± 377.170.1280.0411042.7 ± 380.051024.0 ± 373.180.2260.049 Potassium (mg)2039.23 ± 812.601996.1 ± 780.752087.9 ± 834.782023.9 ± 808.850.1060.0432099.8 ± 849.561998.0 ± 784.050.002*0.124 Iron (mg)10.8 ± 4.5410.7 ± 4.2711.1 ± 4.5210.8 ± 4.70.2560.03511.0 ± 4.811.1 ± 4.700.017*− 0.021 Zinc (mg)9.19 ± 3.899.2 ± 3.6a,b9.5 ± 4.15a9.0 ± 3.81b0.019*0.0579.2 ± 3.879.2 ± 3.900.6890 Copper (mg)1.6 ± 0.731.5 ± 0.611.6 ± 0.751.6 ± 0.760.2540.0551.6 ± 0.711.6 ± 0.740.1700.014
Table 3 Comparison of mean daily food groups and beverages according to sleep duration categories and sleep quality of the study population Data are presented as mean ± standard deviation *p < 0.05 a, b, c There is a statistical difference between the different character Food groups(g/ day)Sleep duration categories(hours/day)Sleep quality Total (n = 2446)< 6 h (n = 449)6-8 h (n = 780)> 8 h (n = 1217)pEffect sizeGood (≤ 5) (n = 1061)Poor (> 5) (n = 1574)Total (n = 2635)pEffect size Milk and dairy product193.8 ± 157.30206.6 ± 165.42a200.6 ± 163.6a,b184.7 ± 150.5b0.014*0.058194.4 ± 153.53193.3 ± 159.86190.7 ± 156.70.8610.007 Meat and pro- cessed meat76.5 ± 86.0677.4 ± 82.86a,b82.8 ± 91.59a72.14 ± 83.34b0.026*0.05572.9 ± 79.3078.9 ± 90.3376.2 ± 88.00.089− 0.070 Egg36.0 ± 40.0235.8 ± 40.8634.8 ± 37.5536.8 ± 41.230.5530.02337.0 ± 41.9035.3 ± 38.6935.8 ± 40.70.3200.042 Bread161.8 ± 122.74157.0 ± 115.11163.5 ± 132.25161.2 ± 119.120.4930.018165.8 ± 123.86159.09 ± 121.94160.5 ± 123.70.1840.055 Cereals142.8 ± 113.51138.9 ± 115.49144.3 ± 110.62143.4 ± 114.650.7080.016141.2 ± 114.08143.9 ± 113.14140.6 ± 113.80.559− 0.024 Legumes32.6 ± 60.8632.6 ± 57.0733.6 ± 59.3431.9 ± 63.170.8330.01333.37 ± 60.5832.07 ± 61.0732.7 ± 62.20.6020.021 Nuts7.6 ± 22.647.6 ± 21.87.8 ± 21.97.4 ± 23.100.9040.0087.0 ± 21.168.0 ± 23.597.3 ± 22.20.296− 0.045 Vegetable200.6 ± 167.43203.5 ± 144.06205.7 ± 177.14196.33 ± 169.070.4370.027205.8 ± 181.90197.1 ± 156.78198.8 ± 166.60.2100.051 Fruit184.03 ± 187.87163.4 ± 203.90184.20 ± 192.74184.0 ± 187.870.1190.041193.3 ± 202.01171.4 ± 185.36175.5 ± 190.60.006*0.113 Fat26.6 ± 18.2426.2 ± 17.1827.5 ± 20.0926.2 ± 17.350.2280.03427.3 ± 19.3026.2 ± 14.4826.4 ± 19.60.1470.064 Sugar15.7 ± 24.9316.9 ± 29.6316.2 ± 25.7115.0 ± 22.400.3110.02916.4 ± 24.415.2 ± 25.2615.4 ± 24.90.2750.048
Limitations
Studying over large sample sizes brings some limitations.
The most important one is that the result of a hypothesis test may demonstrate a significant difference. In this case, it is important to report effect sizes which is independent of sample size besides p values. For this reason, we added effect sizes of all results according to Cohen’s guidelines.”
In the present study, self-reports were taken into account when assessing sleep duration and dietary intake.
In terms of dietary intake, a single administration of 24-h is unable to account for day-to-day variation, two or more non-consecutive recalls could better reflect to estimate usual dietary intake distributions.
Objective assessment methods such as polysomnogra- phy and actigraphy were not utilized in this study to meas- ure sleep duration. Another limitation is that leptin and ghrelin, which are hormones of appetite that are important factors in sleep duration, were not evaluated in this study.
The evaluation of these hormones may make an important contribution to future studies.
Acknowledgements We would like to thank all the participants in the trial for their enthusiastic and maintained collaboration.
Funding This research did not receive any specific grant from funding agencies in the industry, commercial, or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest All authors declare that they have no conflict of interest.
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