ABSTRACT
-
Objective
Smartphone use, psychological stress, and sleep quality are increasingly recognized as lifestyle factors that may influence dietary behaviors in young adults; however, their associations with overall diet quality remain unclear.
-
Methods
This cross-sectional study investigated the associations of daily smartphone use, perceived stress, and perceived sleep quality with diet quality and macronutrient intake among 497 Korean adults aged 20–39 years. Diet quality was assessed using the Nutrition Quotient for Korean Adults 2021 (NQ-2021), and dietary intake was evaluated using a 24-hour dietary recall.
-
Results
Longer smartphone use was inversely associated with total NQ and practice scores after adjustment for potential confounders. Participants with higher perceived stress consumed a greater amount of carbohydrates and a lower amount of fat per 1,000 kcal, whereas total NQ scores did not differ according to perceived stress level. Participants reporting poorer sleep quality had significantly lower total NQ, balance, and moderation scores and lower protein intake than those with better sleep quality.
-
Conclusion
These findings suggest that self-reported smartphone use, perceived stress, and perceived sleep quality are associated with different aspects of diet quality and macronutrient intake among Korean young adults. Prospective studies using validated or objective measures are warranted to clarify the temporal relationships between these lifestyle-related factors and dietary behaviors.
-
Keywords: Life style; Smartphone; Stress, psychological; Sleep quality; Diet
INTRODUCTION
Young adulthood is a transitional period during which individuals increasingly make independent decisions regarding food selection, meal timing, and other health-related behaviors. However, this period is characterized by high rates of skipping meals, irregular eating, low dietary quality, and high dependence on food delivery services and processed convenience foods due to a lack of experience, financial difficulties, and an inability to manage one’s own lifestyle [
1-
3]. These dietary behaviors may increase the risk of inadequate or imbalanced nutrient intake. Therefore, identifying modifiable lifestyle factors associated with diet quality in young adults is important for promoting healthy dietary behaviors and nutritional status.
In South Korea, psychological stress is relatively high for young adults at the beginning of their careers because of a competitive social atmosphere, low employment rates among youth, other reasons. Stress influences the eating behaviors of young adults, which can result in undereating or overeating [
4-
6]. It has been reported that acute stress can cause loss of appetite resulting in undereating, whereas chronic stress is associated with an increased preference for palatable foods that are high in sugar and fat [
5].
According to the 2025 Korea Media Panel Survey, smartphone ownership reached 97.1% among Korean respondents, and the average daily time spent using mobile phones, predominantly smartphones, was 2 hours and 32 minutes, highlighting the pervasive role of smartphones in everyday life [
7]. Smartphones allow users to access the internet, social media, and video content anytime and anywhere. Their widespread availability may contribute to prolonged smartphone use and greater dependence among young adults. Excessive smartphone use has been associated with disruptions in daily functioning and poorer sleep quality [
8]. Prolonged smartphone use may also be associated with less favorable eating behaviors by distracting users during meals, disrupting regular meal patterns, or increasing exposure to food-related digital content [
9,
10]. However, evidence regarding the association between daily smartphone use and overall diet quality, particularly when assessed using validated dietary quality indices, remains limited among young adults.
Perceived stress and sleep quality are also closely related to dietary behaviors and nutrient intake. Psychological stress may alter appetite and food choice, whereas poor sleep quality and short sleep duration have been associated with greater consumption of energy-dense foods and less favorable dietary patterns [
11,
12]. Conversely, dietary factors, including overall diet quality and macronutrient composition, may influence sleep outcomes, suggesting that the relationship between sleep and diet may be bidirectional [
12,
13]. Nevertheless, smartphone use, perceived stress, and perceived sleep quality have generally been examined separately in relation to dietary behaviors.
Therefore, this study simultaneously examined the associations of three common lifestyle-related factors–daily smartphone use, perceived stress, and perceived sleep quality–with overall diet quality and macronutrient intake among Korean adults in their 20s and 30s. We hypothesized that longer smartphone use, higher perceived stress, and poorer perceived sleep quality would be associated with lower diet quality and less favorable macronutrient intake.
METHODS
Study participants
This cross-sectional study was conducted on healthy Korean adults in their 20s and 30s who had no diagnosed medical conditions and were not taking any medications. After the purpose and content of the study were explained, participants who understood the study and agreed to participate were selected. After an interview with the researcher, participants completed a self-administered survey conducted between November 17, 2022, and June 9, 2023. This cross-sectional study calculated the required sample size using the formula for estimating a population proportion in an infinite population. Assuming a 95% confidence level and a 5% margin of error, the minimum required sample size was 384 participants. Considering the estimated dropout rate and the statistical estimation, 539 participants were recruited for the study. Of these, 42 participants were excluded due to incomplete responses or dietary intake-related exclusion criteria, and the final data from 497 participants were included in the statistical analysis. This study was approved by the Institutional Review Board of Kongju National University (KNU_IRB_2022-104). Written informed consent was obtained from all participants, and all procedures were performed in accordance with research ethics regulations and guidelines.
Survey on general characteristics and lifestyle-related factors
The questionnaire consisted of questions related to the general characteristics and lifestyle-related factors of the participants. The general characteristics included sex, age, physical characteristics (height, weight), occupation, residence type, and monthly income. Lifestyle-related variables were assessed using three single-item questions: daily smartphone usage time, perceived stress level, and perceived sleep quality. These items were not intended to constitute a standardized or validated lifestyle assessment questionnaire; rather, they were used to assess selected lifestyle-related characteristics of the participants. Stress levels were assessed on a 5-point Likert scale ranging from “feeling a great deal of stress” to “hardly feeling any stress,” while sleep quality was evaluated using a 5-point scale ranging from “very good” to “very poor.” For statistical analysis, the responses were reclassified into three categories by merging the two extreme ends of each scale. For stress level, the three categories were a little, moderate, and a lot; for sleep quality, the three categories were good, fair, and poor. Body mass index (BMI) was calculated from weight (kg) divided by height squared (m2).
Evaluation of dietary quality via the nutrition quotient for Korean adults
The dietary quality of the participants was assessed using the nutrition quotient (NQ) for Korean adults developed by the Korean Nutrition Society [
14,
15]. The NQ-2021 consists of 18 items assessing three factors: balance, moderation, and practice. The balance factor was composed of eight questions on vegetables, fruits, milk and dairy, fish, legumes, nuts, whole grains and mixed grains, and breakfast. The moderation factor consisted of six questions about the consumption of red meat, processed meat, spicy and salty brothy foods, fast food, fatty breads and sweets, and the frequency of overeating and/or binge eating. The practice factor consisted of four questions on effort to eat healthy, reading nutrition labels, hand washing, and frequency of binge drinking. On the basis of the participants’ responses to 18 questions, the NQ score was calculated by applying the itemized scores and weights proposed in the study [
15]. The calculated NQ score assesses the dietary quality of the participants, with higher values indicating better dietary quality.
Dietary intake was assessed using a single 24-hour dietary recall covering all foods and beverages consumed on the day preceding the survey. To ensure consistency in data collection, all 24-hour dietary recalls were conducted by one trained interviewer based on the individual 24-hour dietary recall questionnaire (Food Intake Survey Form II) and the dietary assessment procedures used in the Korea National Health and Nutrition Examination Survey (KNHANES) [
16]. Each participant was asked to recall the type and amount of all foods and beverages consumed, including individual food items and dishes. To identify factors that might affect usual dietary intake, four additional questions were asked regarding whether the reported intake was representative of the participant’s usual dietary pattern, whether the participant regularly took vitamin or mineral supplements, whether the participant was restricting their diet, and whether the participant followed a special dietary pattern. Participants with incomplete responses, those whose reported dietary intake was considered unrepresentative of their usual intake, and those who were following a weight-control, therapeutic, or other special diet were excluded from the analysis. After these exclusions, 497 participants were included in the final statistical analysis. Based on the 24-hour dietary recall data, daily intakes of energy and energy-yielding nutrients were analyzed using CAN-Pro 5.0 (Computer Aided Nutritional Analysis Program version 5.0; The Korean Nutrition Society).
Statistical analysis was performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Frequencies, means, and standard deviations were calculated for all the variables. Differences in variables according to lifestyle status were verified via the chi-square test and Fisher exact test for noncontinuous variables and via the unpaired t-test and analysis of variance (ANOVA) for continuous variables. For the ANOVA test, post hoc tests were performed with Duncan's multiple range test when there was a significant difference. In addition, the correlations between variables were verified via a partial correlation test after adjusting for age, residence type, occupation, and income. All statistical significance was tested at the P<0.05 level.
RESULTS
General characteristics
The general characteristics of the subjects are shown in
Table 1. The mean age of the subjects was 27.27 years, and there was no significant difference in age between men and women. Height, weight, and BMI were significantly greater in men than in women (P<0.001). The distribution of occupations differed significantly by sex, with a higher percentage of technical jobs for men and service jobs for women (P<0.001). Residence type and monthly income were not significantly different between men and women.
Table 2 shows the lifestyle-related behaviors of the subjects. The greatest percentage of daily smartphone usage time was 4 to <6 hours at 37%, and the greatest percentage regarding quality of sleep was fair at 44%, without a significant difference between men and women. However, perceived stress levels were significantly different between men (45% moderate, 32% a little) and women (41% moderate, 37% a lot) (P=0.001).
Dietary quality and nutritional intake status
Table 3 shows the dietary quality assessed via the NQ score and dietary intake of the subjects. The total NQ score of the subjects was significantly higher in women than in men (46.93 vs. 43.68; P<0.001). In addition, the score for the practice factor was significantly higher for women than for men (54.75 vs. 48.52; P<0.001). Daily energy intake was significantly greater for men (1,966.89 kcal) than for women (1,736.36 kcal). Carbohydrate and fat intakes per 1,000 kcal were not significantly different between men and women, but protein intake per 1,000 kcal was significantly greater in men than in women (39.15 g/1,000 kcal vs. 36.81 g/1,000 kcal; P=0.029).
Associations of smartphone use with dietary quality and nutritional intake
The correlations of smartphone usage time with the NQ score and dietary intake are shown in
Table 4. Daily smartphone use was negatively correlated with the total NQ score (r=−0.140, P=0.002) and practice score (r=−0.150, P<0.001) in the overall sample. In sex-specific analyses, the practice score was negatively correlated with smartphone use in both men (r=−0.134, P=0.037) and women (r=−0.187, P=0.003). The total NQ score was negatively correlated with smartphone use in men (r=−0.168, P=0.009), but not in women (r=−0.118, P=0.063). Additionally, the moderation score was negatively correlated with smartphone use in men (r=−0.175, P=0.006).
The NQ score and dietary intake according to perceived stress level are shown in
Table 5. Overall, participants who perceived stress a lot had significantly greater carbohydrate intake and significantly lower fat intake than those who perceived stress moderately or a little. By sex, this pattern was found in men but not in women.
The NQ score and dietary intake according to perceived sleep quality are shown in
Table 6. For all subjects, the total NQ score was significantly higher in the good sleep-quality group than in the fair and poor sleep-quality groups. The balance and moderation scores were significantly higher in the good sleep-quality group than in the poor sleep-quality group (P=0.038, P=0.019, respectively), whereas the fair group did not differ significantly from either group. Fat intake was significantly higher in the good sleep-quality group than in the fair group (P=0.041), while protein intake was significantly lower in the poor sleep-quality group than in the good and fair groups (P=0.038). Among women, the total NQ score was significantly higher in the good sleep-quality group than in the fair and poor sleep-quality groups (P=0.015), whereas the moderation score was significantly higher in the good group than in the poor group (P=0.041). No significant differences in NQ scores or dietary intake according to perceived sleep quality were observed among men.
DISCUSSION
This study simultaneously evaluated three common lifestyle-related factors and demonstrated that each was associated with different dimensions of diet quality and macronutrient intake among Korean young adults. Rather than showing consistent associations across all dietary outcomes, each lifestyle-related factor was associated with different dimensions of diet quality and nutrient intake.
In terms of smartphone usage and dietary behavior, it has been reported that adolescents who exhibit addictive smartphone use are more likely to experience disordered eating habits such as meal skipping, low consumption of vegetables and fruits, and food addiction [
17,
18]. In our study, the total NQ and practice scores demonstrated a significant negative correlation with smartphone usage time for all the subjects and for men. These results may be attributed to skipping meals due to smartphone use or exposure to poor dietary habits through advertisements on smartphone and Mukbang videos, as the most frequently advertised food products are energy-dense, nutritionally deficient items such as fast food and sugar-sweetened beverages [
18,
19].
As the subjects’ perceived stress levels increased in our study, carbohydrate intake per 1,000 kcal increased, and fat intake per 1,000 kcal decreased, especially in men. In humans, the psychological stress response is difficult to characterize since it tends to be a blend of emotion and threat and challenge appraisal. Previous experimental studies and a recent systematic review have reported that acute psychological stress is associated with increased consumption of energy-dense and unhealthy foods while reducing the intake of healthier food choices [
20]. Another study reported preliminary findings suggesting that threat-related stress may increase food intake, particularly calorie-dense food consumption, compared with positive challenge stress [
21]. These findings differ from those of the present study, which did not show an association between perceived stress levels and energy intake or the intake of energy-dense nutrients such as fat. This discrepancy may be explained by differences in the type and duration of stress, the use of experimentally induced acute stress versus self-perceived stress in daily life, and the characteristics of the study populations. Because perceived stress was assessed using a single self-reported item, the present findings should be interpreted cautiously. Nevertheless, the results suggest that perceived stress may be associated with the relative composition of macronutrient intake rather than total energy intake.
One possible explanation for the higher carbohydrate intake observed among participants with greater perceived stress involves serotonergic regulation of appetite and macronutrient preference. Carbohydrate intake may alter the availability of tryptophan for serotonin synthesis, which has been proposed as a mechanism underlying stress-related carbohydrate preference [
22,
23]. Carbohydrate intake is known to increase serotonin synthesis, whereas protein intake either decreases serotonin levels or has no effect [
24]. However, this mechanism was not assessed in the present study, and the cross-sectional design does not allow conclusions regarding whether stress influenced carbohydrate intake or vice versa.
Poorer perceived sleep quality was associated with lower total NQ, balance, and moderation scores, whereas carbohydrate intake did not differ significantly across sleep-quality groups. Previous studies have reported that both dietary quality and carbohydrate quality, including glycemic index and the degree of food processing, may be associated with sleep outcomes [
12,
25]. For example, high-glycemic-index meals and diets rich in added sugars and refined grains have been associated with sleep onset and insomnia risk, whereas greater consumption of fiber, whole grains, fruits, and vegetables has been associated with more favorable sleep outcomes [
26,
27]. Because these aspects of carbohydrate quality were not assessed in the present study, the absence of an association with total carbohydrate intake should be interpreted cautiously.
Some studies have indicated that nutrients such as protein offer specific sleep benefits, including promoting the onset of sleep and reducing nighttime awakenings [
25,
28]. Previous studies have reported that both low and high protein intake may be associated with different sleep disturbances and that dietary tryptophan and other amino acids may be related to sleep duration and quality [
29,
30]. These findings are consistent with a possible association between protein intake and sleep; however, the relationship is likely bidirectional, and the cross-sectional design of the present study does not allow the temporal direction to be determined. Overall, the findings support associations of perceived sleep quality with diet quality and protein intake rather than establishing a causal effect of diet on sleep.
This study has several limitations. First, the cross-sectional design precludes conclusions regarding causality or the temporal direction of the observed associations. Second, smartphone use, perceived stress, and perceived sleep quality were assessed using single self-reported items rather than validated multi-item instruments or objective measures, which may have introduced measurement error and misclassification. In addition, categorizing the original 5-point responses into three groups may have reduced variability in the data. Third, dietary intake was assessed using a single 24-hour recall, which may not adequately reflect habitual intake because of day-to-day variation. Fourth, the high proportion of students and the recruitment of participants from a limited population may restrict the generalizability of the findings. Fifth, residual confounding by factors such as physical activity, smoking, alcohol consumption, chronotype, and habitual sleep duration cannot be excluded. Future prospective studies should incorporate objective smartphone-use records, validated instruments such as the Perceived Stress Scale and Pittsburgh Sleep Quality Index, actigraphy or other objective sleep measures, and repeated dietary recalls or food records. Sixth, perceived stress and sleep quality were examined as separate lifestyle factors, and their potential interaction was not evaluated. Given the close relationship between psychological stress and sleep quality, future studies should investigate whether their combined or interactive effects are associated with diet quality and macronutrient intake. Finally, the analyses presented in
Tables 4-
6 involved multiple statistical comparisons without adjustment for multiple testing; therefore, these findings should be interpreted as exploratory.
In conclusion, smartphone use, perceived stress, and perceived sleep quality were differentially associated with diet quality and macronutrient intake among Korean young adults. Longer smartphone use was associated with lower total NQ and practice scores, higher perceived stress was associated with greater carbohydrate and lower fat intake, and poorer perceived sleep quality was associated with lower overall diet quality and protein intake. These findings may help inform lifestyle-based nutrition strategies for improving diet quality among Korean young adults.
NOTES
-
Author Contributions
Conceptualization: MKC. Data curation and analysis: MHK, EK, MKC. Investigation: MKC. Methodology: MHK, MKC. Writing - original draft: MHK, EK, MKC. Writing - review & editing: MHK, EK, MKC. All authors read and approved the final manuscript.
-
Conflict of Interest
Mi-Kyeong Choi is an editorial board member of this journal, but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflict of interest relevant to this article was reported.
-
Data availability
The datasets used and/or analyzed during the current study are not publicly available because they contain sensitive participant information. Data are available from the corresponding author upon reasonable request and with appropriate ethical approval.
Table 1.General characteristics of the subjects
Table 1.
|
Variable |
Total (n=497) |
Men (n=246) |
Women (n=251) |
χ²(t)-value |
P-value |
|
Age (yr) |
27.27±6.47 |
27.43±6.43 |
27.11±6.51 |
0.54 |
0.587 |
|
Height (cm) |
168.01±8.57 |
174.52±5.93 |
161.63±5.36 |
25.44 |
<0.001 |
|
Weight (kg) |
67.20±14.63 |
75.08±12.54 |
59.49±12.24 |
14.03 |
<0.001 |
|
Body mass index (kg/m2) |
23.65±4.06 |
24.59±3.58 |
22.72±4.30 |
5.28 |
<0.001 |
|
Residence typea)
|
|
|
|
2.78 |
0.410 |
|
With family |
186 (37.42) |
87 (35.37) |
99 (39.44) |
|
|
|
Alone |
174 (35.01) |
83 (33.74) |
91 (36.25) |
|
|
Dormitory |
133 (26.76) |
74 (30.08) |
59 (23.51) |
|
|
Others |
4 (0.80) |
2 (0.81) |
2 (0.80) |
|
|
Occupation |
|
|
|
50.54 |
<0.001 |
|
Profession |
43 (8.65) |
20 (8.13) |
23 (9.16) |
|
|
|
Technician |
41 (8.25) |
38 (15.45) |
3 (1.20) |
|
|
Service |
67 (13.48) |
15 (6.10) |
52 (20.72) |
|
|
Student |
274 (55.13) |
136 (55.28) |
138 (54.98) |
|
|
Others |
72 (14.49) |
37 (15.04) |
35 (13.94) |
|
|
Income (10,000 won/mo) |
|
|
|
1.68 |
0.432 |
|
<50 |
162 (32.60) |
74 (30.08) |
88 (35.06) |
|
|
|
50–200 |
139 (27.97) |
69 (28.05) |
70 (27.89) |
|
|
≥200 |
196 (39.44) |
103 (41.87) |
93 (37.05) |
|
Table 2.Lifestyle-related behaviors of the subjects
Table 2.
|
Variable |
Total (n=497) |
Men (n=246) |
Women (n=251) |
χ²-value |
P-value |
|
Daily smartphone usage time |
|
|
|
7.24 |
0.124 |
|
<2 hr |
20 (4.02) |
13 (5.28) |
7 (2.79) |
|
|
|
2 to <4 hr |
115 (23.14) |
65 (26.42) |
50 (19.92) |
|
|
4 to <6 hr |
184 (37.02) |
87 (35.37) |
97 (38.65) |
|
|
6 to <8 hr |
97 (19.52) |
40 (16.26) |
57 (22.71) |
|
|
≥8 hr |
81 (16.30) |
41 (16.67) |
40 (15.94) |
|
|
Perceived stress level |
|
|
|
13.74 |
0.001 |
|
A little |
134 (26.96) |
79 (32.11) |
55 (21.91) |
|
|
|
Moderate |
214 (43.06) |
111 (45.12) |
103 (41.04) |
|
|
A lot |
149 (29.98) |
56 (22.76) |
93 (37.05) |
|
|
Perceived sleep quality |
|
|
|
2.68 |
0.261 |
|
Good |
155 (31.19) |
80 (32.52) |
75 (29.88) |
|
|
|
Fair |
219 (44.06) |
113 (45.93) |
106 (42.23) |
|
|
Poor |
123 (24.75) |
53 (21.54) |
70 (27.89) |
|
Table 3.Nutrition quotient score and dietary intake of the subjects
Table 3.
|
Variable |
Total (n=497) |
Men (n=246) |
Women (n=251) |
t-value |
P-value |
|
NQ score |
|
|
|
|
|
|
Balance |
25.11±13.92 |
24.63±12.71 |
25.59±15.02 |
–0.77 |
0.442 |
|
Moderation |
57.07±14.29 |
56.29±14.11 |
57.83±14.44 |
–1.21 |
0.228 |
|
Practice |
51.67±17.84 |
48.52±17.11 |
54.75±18.04 |
–3.95 |
<0.001 |
|
Total |
45.32±10.91 |
43.68±10.30 |
46.93±11.26 |
–3.35 |
<0.001 |
|
Daily intake |
|
|
|
|
|
|
Energy (kcal) |
1,850.47±774.53 |
1,966.89±814.93 |
1,736.36±716.20 |
3.35 |
<0.001 |
|
Carbohydrate (g) |
222.12±91.39 |
236.25±96.32 |
208.28±84.21 |
3.44 |
<0.001 |
|
Fat (g) |
63.65±40.46 |
65.35±41.60 |
61.97±39.60 |
0.93 |
0.354 |
|
Protein (g) |
68.82±32.60 |
75.48±34.85 |
62.29±28.83 |
4.59 |
<0.001 |
|
Carbohydrate (g/1,000 kcal) |
124.47±33.36 |
124.14±33.97 |
124.79±32.81 |
–0.21 |
0.831 |
|
Fat (g/1,000 kcal) |
33.23±12.06 |
32.23±11.85 |
34.21±12.22 |
–1.83 |
0.067 |
|
Protein (g/1,000 kcal) |
37.97±11.93 |
39.15±12.33 |
36.81±11.43 |
2.19 |
0.029 |
Table 4.Correlation of smartphone usage with nutrition quotient score and dietary intake of the subjects
Table 4.
|
Variable |
Daily smartphone usage time |
|
Total (n=497) |
Men (n=246) |
Women (n=251) |
|
r |
P-value |
r |
P-value |
r |
P-value |
|
BMI |
0.073 |
0.107 |
0.124 |
0.054 |
0.031 |
0.633 |
|
NQ score |
|
|
|
|
|
|
|
Balance |
–0.062 |
0.168 |
–0.020 |
0.759 |
–0.096 |
0.132 |
|
Moderation |
–0.047 |
0.299 |
–0.175 |
0.006 |
0.105 |
0.101 |
|
Practice |
–0.150 |
<0.001 |
–0.134 |
0.037 |
–0.187 |
0.003 |
|
Total |
–0.140 |
0.002 |
–0.168 |
0.009 |
–0.118 |
0.063 |
|
Energy |
–0.048 |
0.285 |
–0.064 |
0.325 |
0.003 |
0.968 |
|
Carbohydrate (g/1,000 kcal) |
–0.053 |
0.243 |
–0.079 |
0.218 |
–0.025 |
0.700 |
|
Fat (g/1,000 kcal) |
0.018 |
0.694 |
0.030 |
0.640 |
–0.002 |
0.971 |
|
Protein (g/1,000 kcal) |
–0.029 |
0.525 |
0.046 |
0.476 |
–0.080 |
0.207 |
Table 5.Nutrition quotient score and dietary intake according to perceived stress of the subjects
Table 5.
|
Variable |
Perceived stress level |
F-value |
P-value |
|
A little |
Moderate |
A lot |
|
Total (n=497) |
|
|
|
|
|
|
No. of subjects |
134 |
214 |
149 |
|
|
|
BMI (kg/m2) |
24.14±4.40 |
23.25±3.81 |
23.78±4.07 |
2.10 |
0.124 |
|
NQ score |
|
|
|
|
|
|
Balance |
25.09±14.21 |
25.37±13.61 |
24.78±14.18 |
0.08 |
0.923 |
|
Moderation |
58.23±14.97 |
57.35±14.24 |
55.61±13.70 |
1.27 |
0.282 |
|
Practice |
51.54±18.56 |
50.90±17.46 |
52.88±17.78 |
0.54 |
0.581 |
|
Total |
45.61±11.74 |
45.18±10.32 |
45.27±11.03 |
0.07 |
0.934 |
|
Energy (kcal) |
1,897.61±697.86 |
1,878.32±816.07 |
1,768.07±777.41 |
1.23 |
0.293 |
|
Carbohydrate (g/1,000 kcal) |
119.11±31.45b
|
122.53±31.75b
|
132.07±36.06a
|
6.08 |
0.003 |
|
Fat (g/1,000 kcal) |
35.11±11.01a
|
33.36±11.62ab
|
31.35±13.33b
|
3.48 |
0.032 |
|
Protein (g/1,000 kcal) |
37.96±10.68 |
39.04±11.95 |
36.44±12.83 |
2.10 |
0.124 |
|
Men (n=246) |
|
|
|
|
|
|
No. of subjects |
79 |
111 |
56 |
|
|
|
BMI (kg/m2) |
24.70±4.11 |
24.22±3.36 |
25.19±3.14 |
1.42 |
0.243 |
|
NQ score |
|
|
|
|
|
|
Balance |
24.29±13.61 |
25.70±11.98 |
23.01±12.84 |
0.87 |
0.419 |
|
Moderation |
57.51±13.97 |
55.66±14.42 |
55.79±13.82 |
0.44 |
0.645 |
|
Practice |
48.08±16.84 |
48.71±17.27 |
48.77±17.43 |
0.04 |
0.962 |
|
Total |
43.77±11.01 |
43.89±9.73 |
43.15±10.53 |
0.10 |
0.904 |
|
Energy (kcal) |
1,928.19±652.01 |
2,091.15±884.36 |
1,775.20±848.83 |
2.98 |
0.053 |
|
Carbohydrate (g/1,000 kcal) |
120.35±29.68b
|
120.62±32.21b
|
136.50±40.18a
|
4.95 |
0.008 |
|
Fat (g/1,000 kcal) |
34.55±10.25a
|
33.10±11.58a
|
27.22±13.17b
|
7.16 |
0.001 |
|
Protein (g/1,000 kcal) |
39.15±10.28 |
39.50±11.39 |
38.46±16.34 |
0.13 |
0.876 |
|
Women (n=251) |
|
|
|
|
|
|
No. of subjects |
55 |
103 |
93 |
|
|
|
BMI (kg/m2) |
23.34±4.70 |
22.21±4.01 |
22.93±4.33 |
1.41 |
0.246 |
|
NQ score |
|
|
|
|
|
|
Balance |
26.25±15.09 |
25.02±15.23 |
25.84±14.90 |
0.14 |
0.871 |
|
Moderation |
59.27±16.38 |
59.18±13.87 |
55.50±13.69 |
1.95 |
0.145 |
|
Practice |
56.51±19.90 |
53.26±17.43 |
55.35±17.61 |
0.67 |
0.514 |
|
Total |
48.26±12.33 |
46.56±10.79 |
46.54±11.17 |
0.49 |
0.612 |
|
Energy (kcal) |
1,853.68±762.95 |
1,648.96±666.98 |
1,763.78±735.84 |
1.58 |
0.208 |
|
Carbohydrate (g/1,000 kcal) |
117.34±34.03 |
124.59±31.27 |
129.40±33.27 |
2.36 |
0.096 |
|
Fat (g/1,000 kcal) |
35.91±12.08 |
33.64±11.71 |
33.84±12.87 |
0.69 |
0.502 |
|
Protein (g/1,000 kcal) |
36.24±11.16 |
38.55±12.57 |
35.22±10.06 |
2.17 |
0.116 |
Table 6.Nutrition quotient score and dietary intake according to perceived sleep quality of the subjects
Table 6.
|
Variable |
Perceived sleep quality |
F-value |
P-value |
|
Good |
Fair |
Poor |
|
Total (n=497) |
|
|
|
|
|
|
No. of subjects |
155 |
219 |
123 |
|
|
|
BMI (kg/m2) |
23.61±3.93 |
23.52±3.84 |
23.92±4.59 |
0.38 |
0.682 |
|
NQ score |
|
|
|
|
|
|
Balance |
27.08±14.71a
|
25.04±13.11ab
|
22.79±14.05b
|
3.29 |
0.038 |
|
Moderation |
59.13±14.47a
|
57.14±14.53ab
|
54.28±13.22b
|
4.01 |
0.019 |
|
Practice |
54.09±18.08 |
50.23±17.36 |
51.17±18.20 |
2.20 |
0.112 |
|
Total |
47.50±11.57a
|
44.76±10.20b
|
43.59±10.87b
|
5.10 |
0.007 |
|
Energy (kcal) |
1,849.13±775.42 |
1,829.73±796.78 |
1,889.09±737.04 |
0.23 |
0.794 |
|
Carbohydrate (g/1,000 kcal) |
122.33±33.58 |
124.86±33.95 |
126.46±32.10 |
0.55 |
0.577 |
|
Fat (g/1,000 kcal) |
34.78±12.46a
|
31.73±11.70b
|
33.94±11.99ab
|
3.22 |
0.041 |
|
Protein (g/1,000 kcal) |
38.71±12.01a
|
38.79±12.52a
|
35.58±10.42b
|
3.30 |
0.038 |
|
Men (n=246) |
|
|
|
|
|
|
No. of subjects |
80 |
113 |
53 |
|
|
|
BMI (kg/m2) |
24.55±3.51 |
24.19±3.22 |
25.54±4.23 |
2.63 |
0.074 |
|
NQ score |
|
|
|
|
|
|
Balance |
25.82±13.18 |
24.62±11.98 |
22.88±13.53 |
0.85 |
0.428 |
|
Moderation |
57.15±14.73 |
57.39±14.63 |
52.61±11.40 |
2.32 |
0.101 |
|
Practice |
50.74±17.08 |
47.10±17.47 |
48.20±16.32 |
1.07 |
0.343 |
|
Total |
45.19±10.72 |
43.44±10.18 |
41.93±9.77 |
1.66 |
0.192 |
|
Energy (kcal) |
1,951.88±801.37 |
1,966.19±837.37 |
1,991.06±801.35 |
0.04 |
0.964 |
|
Carbohydrate (g/1,000 kcal) |
124.96±34.78 |
123.06±35.03 |
125.23±30.84 |
0.11 |
0.898 |
|
Fat (g/1,000 kcal) |
33.57±11.99 |
30.88±12.15 |
33.07±10.82 |
1.38 |
0.253 |
|
Protein (g/1,000 kcal) |
39.18±11.63 |
40.03±13.73 |
37.23±9.96 |
0.93 |
0.396 |
|
Women (n=251) |
|
|
|
|
|
|
No. of subjects |
75 |
106 |
70 |
|
|
|
BMI (kg/m2) |
22.62±4.13 |
22.82±4.31 |
22.69±4.50 |
0.05 |
0.953 |
|
NQ score |
|
|
|
|
|
|
Balance |
28.42±16.14 |
25.49±14.27 |
22.72±14.53 |
2.65 |
0.073 |
|
Moderation |
61.24±13.97a
|
56.94±14.50ab
|
55.54±14.40b
|
3.23 |
0.041 |
|
Practice |
57.66±18.54 |
53.57±16.69 |
53.42±19.31 |
1.40 |
0.250 |
|
Total |
49.96±11.99a
|
46.16±10.13b
|
44.85±11.55b
|
4.28 |
0.015 |
|
Energy (kcal) |
1,739.52±736.31 |
1,684.25±737.14 |
1,811.89±680.08 |
0.67 |
0.513 |
|
Carbohydrate (g/1,000 kcal) |
119.53±32.24 |
126.78±32.82 |
127.40±33.22 |
1.38 |
0.252 |
|
Fat (g/1,000 kcal) |
36.08±12.88 |
32.63±11.18 |
34.60±12.84 |
1.81 |
0.166 |
|
Protein (g/1,000 kcal) |
38.20±12.47 |
37.46±10.99 |
34.33±10.66 |
2.39 |
0.093 |
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