Chrononutrition behavior during the COVID-19 pandemic and its relationship with body weight among college students

1. Introduction

Eating habits have been a major concern among college students. They are at risk of developing unhealthy eating habits such as breakfast skipping, fast food consumption, and high sugar intake (1) that may hinder their health and psychological wellbeing (2, 3). Skipping breakfast affects the hypothalamic-pituitary function and the reproductive cycle by disturbing the central clock system, which leads to ovarian and uterine malfunction (4). The post-adolescent students’ food habits may deteriorate early in college life due to the large number of students who move in alone. The transition to college life, together with academic and lifestyle challenges, was postulated with these significant changes in their nutrition intake. Unhealthy lifestyles, including poor sleep habits, skipping food, poor eating patterns, increased alcohol intake and reduced physical activity, were also reported among college students (5). Consequently, the incidence of weight gain and increased adiposity during college life was identified in previous meta-analyses (6, 7). Nevertheless, the prevalence of being underweight among young adults and college students remains a major concern (8, 9).

While many factors related to overweight and underweight among college students have been reported, there is a paucity of information on their chrononutrition behavior. The emerging field of chrononutrition provides valuable information on managing food intake across the day. Chrononutrition is the interplay between nutrition and circadian rhythm (10). The two crucial elements are dietary components that regulate the circadian system and meal timings that synchronize misaligned molecular clocks (11). In a healthy human, the circadian rhythms play major physiological functions, including the 24-h biological cycle, behavioral, physical, and mental changes. The timing of eating is tied to the internal 24-h biological timing system (the circadian clock) and influence the metabolic process of the body due to the complex interaction between circadian biology, nutrition, and human metabolism (12). Chrononutrition behavior refers to the behavioral patterns that are likely to influence one’s chrononutrition profile, and these include (i) eating at night, (ii) time-restricted feeding, (iii) breakfast eating, (iv) the timing of the largest meal, (v) the time of evening eating, and (vi) the time between eating and sleep time (13). The chrononutrition behavior may affect an individual’s adiposity, which may be related to the physiological adaptation to sleeping and irregularities in eating following circadian times (14). Despite using less energy, individuals with more irregular eating patterns, were more likely to develop obesity and the metabolic syndrome (14).

Studies have shown that eating at different times of the day may affect one’s Body Mass Index (BMI). Inappropriate eating habits such as skipping breakfast and night eating were associated with a high BMI (15, 16). Eventually, high-calorie intake during breakfast was significantly associated with higher weight loss compared to large consumption of calories during dinner (17). It was also reported that individuals who had a high-calorie intake at later time of the day, were exposed to the risk of developing obesity. This is due to the process of lipogenesis and accumulation of adipose tissues, which tend to occur during the period of the last meal (18). Late eating may increase hunger and altered appetite-regulating hormones and altered adipose tissue gene expression favoring increased lipid storage (19).

However, the habit of skipping breakfast was also reported as a means of reducing total daily energy intake (20, 21). Indeed, it raises questions about meal timing studies such as intermittent fasting and time-restricted feeding, which indicate that skipping or delaying breakfast may reduce body weight (22, 23). The relationship between body weight and the meal timing pattern remains unclear, and it is an important research area to explore. Thus, the present study aimed to investigate the potential association of chrononutrition behavior with BMI among young adults, especially college students. To the best of our knowledge, no regional (Malaysia and Southeast Asia) studies on chrononutrition among college students has been published yet. Eating behavior of college students may carry over to later life, hence early identification related to the behavior and its effect on nutritional status (BMI) should be identified and intervene earlier. Consistent with previous studies, we hypothesized to observe significant differences concerning chrononutrition patterns and body weight status.

2. Materials and methods

2.1. Subjects

A cross-sectional study of chrononutrition behavior and weight status was conducted on college students in Malaysia’s Klang Valley. The recruitment was conducted via social media, and electronic forms of questionnaires were distributed. Inclusion criteria were college students aged 18−35 years, literate in the English language, and having access to the internet. Those individuals with a known diagnosis of sleep disorder were excluded. The sample size was derived using OpenEpi calculated software, with 95% confidents interval and 80% power of the study, and yielded at least 385 participants.

This study was approved by the UiTM Ethics Research Committee [REC/06/2021 (UG/MR/589)] and digital informed consent was obtained from all participants. Data on body weight and height were self-reported, and guidelines on how to measure weight and height correctly were included in the instructions. Data collection was performed in the third quarter of 2021, when Malaysia was still in the COVID-19 pandemic but moving to the endemic transition phase. The lockdown is over, and there was no restriction on going out. During this period, college students still adapted to online learning and physical attendance at the institution was not compulsory.

2.2. Instruments

The Chrononutrition Profile Questionnaire (CP-Q) was used to determine the chrononutrition behavior (13). Data from CP-Q measures the chrononutrition pattern on typical work/school as well as free days and can compute eating misalignment. Regarding the present study, we measured six chrononutrition behaviors: Breakfast skipping, largest meal, evening eating, evening latency, night eating, and eating window. Breakfast skipping refers to days per week during which individuals skip breakfast. The largest meal refers to the meal in which the largest number of calories are consumed. Evening eating refers to the last eating event, while evening latency refers to the duration of time between an individual’s last eating event and sleep onset. Night eating refers to one or more days per week during which individuals wake up in the night to eat. Lastly, the term “eating window” refers to the duration of time between one’s first and last eating events of the day. Results were further scored into three; good, fair, and poor behavior, a scoring cut-off from a study by Engwall (24).

Body Mass Index was computed by dividing weight (kg) by height squared (m²). It was then further classified as: <18.5 kg/m2 underweight, 18.5−24.9 kg/m2 normal, and >25 overweight (25).

2.3. Statistical analysis

All analyses were performed using SPSS statistical software version 22.0 (SPSS Inc., Chicago, IL, USA). The participant’s background including sex, age, height, weight, and BMI were reported descriptively in mean (standard deviation) or frequency. Normality tests were conducted before parametric analyses. One-way ANOVA was used to investigate differences in mean intake (days) of breakfast, snacking after last meals, and night eating between the different BMI groups. Pearson chi-squared analyses were used to determine the differences between BMI and chrononutrition behaviors. All significant variables (p < 0.05) were then analyzed in a logistic regression model to determine the predictor of BMI.

3. Results

A total of 409 participants with a mean age of 21.45 ± 2.2 years completed the questionnaire. Most of the participants were females (89.5%), and almost half of them were of normal weight (49.4%). The mean BMI was 22.4 ± 5.13 kg/m². A quarter of our participants were either underweight (24.7%) or overweight (25.9%) (Table 1).

TABLE 1

Table 1. Background of participants.

As presented in Table 2, mean intake (days per week) of breakfast and snacking after the last meal showed no significant differences between BMI categories. However, we found that the underweight group significantly had a higher intake (days per week) of night eating compared to normal weight group (p < 0.05). The largest meal consumed by all BMI categories was during lunch (Table 3). Timing of sleep during both free days and workdays, did not show any different between BMI. However, observing the sleeping patterns of the participants revealed that half of them sleep after midnight even on workdays. During free days, the participants were more likely to sleep after midnight.

TABLE 2

Table 2. Mean intake (days per week) of breakfast, snacking after last meal, and eating at night.

TABLE 3

Table 3. Association between largest meal of the day and timing of sleep with BMI.

Chi-square analyses revealed a significant different between BMI categories and eating window, evening latency, evening eating, and night eating (p < 0.05) (Table 4). It was found that being underweight was associated with poor eating habits compared to being normal and overweight. Further analyses using logistic regression identified that significant predictors of being underweight included poor eating window, poor evening latency, poor evening eating, and poor night eating (Table 5). All this poor chrononutrition behavior was strongly related to being underweight.

TABLE 4

Table 4. Association between the scoring of chrononutrition behavior with BMI.

TABLE 5

Table 5. Predictors of being underweight or overweight.

4. Discussion

Chrononutrition is closely related to the individual body’s circadian rhythm; the biological clock that regulates the sleep and wake cycle. Its response is highly influenced by changes in the environment, primarily to light and darkness. It may not be forgotten that the light/dark cycle and food intake are important circadian rhythm regulators controlled by the central clock system. These oscillations that occur in the body influence physical, mental, and behavioral changes following a 24-h cycle (18). On the other hand, the peripheral clocks in each body system that controls localized physiological processes which include glucose and lipid homeostasis, hormonal secretion, the immune responses, and the digestive system is highly influenced by the nutritional intake and physical activity patterns (26). Current studies suggest that eating time highly influences body weight; specifically, eating meals late at night may impact the desynchronization of the internal biological clock. Charlot et al. (27) suggested to prioritize in matching daily application of eating time to individual’s circadian rhythms for optimal metabolic health (27). Important circadian hormones affecting body weight, namely cortisol, serotonin, melatonin, insulin, and insulin growth factor 1 (IGF-1) are highly synchronized with the biological clock. Hence, desynchronization of meal intake will further affect these hormones and is phenotypically reflected in body weight (28).

The results of the present study found that more than 24% of participants had their largest meal at night during dinner or supper. The prevalence is in accordance with the current trend worldwide seen among young adults. Swiss adults aged 18−26 years (29), US college students (30), and Turkish university students (31) were all found to have a similar trend of night eating. Despite the fact that night eating is associated with increased body weight (15), the young adults who were underweight in our sampling frame, showed a significantly higher prevalence of night eating. Zooming at other related research that found the association between obesity and night eating, the sampling frame focused on adults at the age of more than 30 years (15, 32, 33). Guentcheva et al. (34) also found that young adults with night eating problems have lower BMI compared to those without the problem. However, the difference was not statistically significant (34). Another population of young adults that was similar to the present study was the finding among Pakistan college students who were underweight and had poorer eating habits than those with normal BMI and overweight (35). Another important aspect that must be considered in a self-reporting questionnaire to assess dietary habits is the bias that may exist in the accuracy of habitual nutritional behaviors (36). Thus, this may suggest that the effect may arise later in life. To establish the cause and effect, another important factor that must be included in future studies is the duration of individual eating habits being established.

It is important to highlight that altered food intake as shown among these young adults may affect their hormonal patterns in the long run. Since the human body has the capability of adapting and compensating for changes, the changes may not be apparent at an early age. However, recent studies have started to discuss the potential ripple effects of poor chrononutrition on health at a later age. Evidence shows that insulin sensitivity is highly associated with circadian regulation; hence, the thermic effect of food is reduced in the evening. Therefore, it was suggested that blood sugar and insulin responses to carbohydrates are more exaggerated at night than during the day (37). Recent studies suggest that endometriosis, commonly manifested by dysmenorrhea, arises as a result of the modern dietary lifestyle, thus showing the evidence of gynecological disorders being closely linked to dietary practices (38, 39). It has been reported that female students who skipped breakfast had a higher incidence of dysmenorrhea (40).

Theoretically, the time frame of intake would be higher among individuals with short sleep duration because the total caloric intake is directly associated with the time spent awake (41). The regulation will be in synchrony with normal physiological adaptation to the environmental influence (42). However, no significant differences were observed in participants, eating behaviors with their sleep duration. The finding is consistent with the findings obtained among American (43) and Brazilian adults (44).

The present study found that there was a vast heterogeneity in sleeping patterns among each participant. Benham (45) reported that the COVID-19 pandemic had a significant effect on students’ sleeping patterns. The study found that students went to bed significantly later during the pandemic and there were pronounced delays in waketimes (45). Since the present study was conducted during the pandemic after the lockdown, the wide range of waking time and sleeping time shown in the study population may have been affected by the asynchronous format of their classes’ format. Additionally, during the sampling, some of the undergraduates were still having online classes, hence, eliminating the need for early waketime.

Based on Abraham et al. (1), young adults usually establish their eating habits during their years in college and the behavior often continues through adulthood. Furthermore, Das and Evans (46) highlighted that body weight is part of the barriers and promoting factors for lifestyle choices to maintain health. The best eating habits and ideal weight can only be achieved with the help of proper knowledge about nutrition, but this knowledge needs to be combined with favorable environmental conditions such as access to nutritious food and physical activity activities (47, 48). In earlier studies, it has been reported that the absence of healthy food in educational institutions was a barrier to healthy eating (49). Self-efficacy, dietary preferences, body image, conformity to friends and parents, socioeconomic position, and the accessibility of food in the community are a few of the variables that may directly or indirectly influence adolescent eating behaviors (50).

Interestingly, earlier studies have also reported the fact that adolescents and young adults also had the habit of not consuming a proper lunch until they returned home at 03.00−04.00 p.m. (49). It has been reported that individuals who receive parental encouragement and support, tend to eat healthier foods and have better eating habits (51, 52). The underweight participants are less particular in their eating time frame (46). Good eating habits may not be the main priority being emphasized by those underweight. However, they may suffer from health consequences at a later age if their eating habits continue. Result from this study opens a new gap of study that warrant serious attention. Despite plethora of publications on eating habits of university students, most studies focused on eating disorders among those who are overweight and obese (7, 53). However, cohort studies are needed to elucidate the impact of eating disorders among those who are underweight and its association with their later health impact.

We admit a few limitations in our study. The cross-sectional design employed may be insufficient to arrive at any definite conclusion. Our study had a small sample size, hence unable to generalize to all college students in Malaysia. Besides that, participation in this study is voluntary basis, hence, resulted in marked volume of women participants as compared to the men. It was earlier reported that there are large variety in eating behavior among college students with conflicting results on the differences reflected between gender (54, 55). As weight and height data were reported, it may introduce bias, which is the limitation of all self-reported surveys. However, we provided an insight into the situation happening in the younger population. It is paramount to include the underweight in any nutrition intervention as well, as they are prone to poor eating habits.

5. Conclusion

This study found that underweight college students have poor chrononutrition behavior as compared to the rest. The general lack of knowledge regarding healthy and timely eating among college students is a cause of concern. These population of interest tend to skip meals and develop various eating disorders. These young adults are constantly exposed to unhealthy lifestyles and poor dietary choices without knowing the consequences of such exposure. The dietary habits of this population can be duly addressed with proper education and health screening programs. There is also a need to build strong social support and a framework to promote healthy eating among college-going young adults. Future long-term research studies should be conducted to arrive at a definite conclusion.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by the Research Ethics Committee, Universiti Teknologi MARA. The patients/participants provided their written informed consent to participate in this study.

Author contributions

NT designed the research, supervised the project, and wrote the manuscript. NJ contributed to the research design and wrote the manuscript. KH conducted the experiment and drafted the manuscript. WW conducted the experiment. SD edited and revised the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We gratefully acknowledge the Universiti Teknologi MARA [600-RMC/YTR/5/3 (001/2022)] and Universiti Sains Islam Malaysia and thank all the responders who participated in this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Abraham S, Noriega B, Shin J. College students eating habits and knowledge of nutritional requirements. J Nutr Hum Health. (2018) 2:13–7.

Google Scholar

2. Wattick R, Hagedorn R, Olfert M. Relationship between diet and mental health in a young adult Appalachian college population. Nutrients. (2018) 10:957. doi: 10.3390/nu10080957

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Hong S, Peltzer K. Dietary behavior, psychological well-being and mental distress among adolescents in Korea. Child Adolesc Psychiatry Ment Health. (2017) 11:1–12. doi: 10.1186/s13034-017-0194-z

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Fujiwara T, Nakata R. Skipping breakfast is associated with reproductive dysfunction in post-adolescent female college students. Appetite. (2010) 55:714–7. doi: 10.1016/j.appet.2010.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Kanikowska D, Sikorska D, Kuczyńska B, Grzymisławski M, Brȩborowicz A, Witowski J. Do medical students adhere to advice regarding a healthy lifestyle? A pilot study of BMI and some aspects of lifestyle in medical students in Poland. Adv Clin Exp Med. (2017) 26:1391–8. doi: 10.17219/acem/65783

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Fedewa M, Das B, Evans E, Dishman R. Change in weight and adiposity in college students: a systematic review and meta-analysis. Am J Prev Med. (2014) 47:641–52. doi: 10.1016/j.amepre.2014.07.035

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Vadeboncoeur C, Townsend N, Foster C. A meta-analysis of weight gain in first year university students: is freshman 15 a myth? BMC Obesity. (2015) 2:22. doi: 10.1186/s40608-015-0051-7

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Peltzer K, Pengpid S, Samuels T, Özcan N, Mantilla C, Rahamefy O, et al. Prevalence of overweight/obesity and its associated factors among university students from 22 countries. Int J Environ Res Public Health. (2014) 11:7425–41.

Google Scholar

9. Abarca-Gómez L, Abdeen Z, Hamid Z, Abu-Rmeileh N, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. (2017) 390:2627–42. doi: 10.1016/S0140-6736(17)32129-3

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Tahara Y, Shibata S. Chrono-biology, chrono-pharmacology, and chrono-nutrition. J Pharmacol Sci. (2014) 124:320–35. doi: 10.1254/jphs.13r06cr

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Ahluwalia M. Chrononutrition—When We Eat Is of the Essence in Tackling Obesity. Nutrients. (2022) 14:5080. doi: 10.3390/nu14235080

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Almoosawi S, Vingeliene S, Gachon F, Voortman T, Palla L, Johnston J, et al. Chronotype: implications for epidemiologic studies on chrono-nutrition and cardiometabolic health. Adv Nutr. (2019) 10:30–42. doi: 10.1093/advances/nmy070

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Veronda A, Allison K, Crosby R, Irish L. Development, validation and reliability of the Chrononutrition Profile-Questionnaire. Chronobiol Int. (2020) 37:375–94.

Google Scholar

14. Pot G, Almoosawi S, Stephen A. Meal irregularity and cardiometabolic consequences: results from observational and intervention studies. Proc Nutr Soc. (2016) 75:475–86. doi: 10.1017/S0029665116000239

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Okada C, Imano H, Muraki I, Yamada K, Iso H. The association of having a late dinner or bedtime snack and skipping breakfast with overweight in Japanese women. J Obesity. (2019) 2019:2439571.

Google Scholar

16. Ma X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, et al. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. Obesity Res Clin Pract. (2020) 14:1–8.

Google Scholar

17. Reid K, Baron K, Zee P. Meal timing influences daily caloric intake in healthy adults. Nutr Res. (2014) 34:930–5.

Google Scholar

18. Mohd Azmi N, Juliana N, Mohd Fahmi Teng N, Azmani S, Das S, Effendy N. Consequences of circadian disruption in shift workers on chrononutrition and their psychosocial well-being. Int J Environ Res Public Health. (2020) 17:2043. doi: 10.3390/ijerph17062043

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Vujović N, Piron M, Qian J, Chellappa S, Nedeltcheva A, Barr D, et al. Late isocaloric eating increases hunger, decreases energy expenditure, and modifies metabolic pathways in adults with overweight and obesity. Cell Metab. (2022) 34:1486–98. doi: 10.1016/j.cmet.2022.09.007

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Levitsky D, Pacanowski C. Effect of skipping breakfast on subsequent energy intake. Physiol Behav. (2013) 119:9–16.

Google Scholar

21. Sievert K, Hussain S, Page M, Wang Y, Hughes H, Malek M, et al. Effect of breakfast on weight and energy intake: systematic review and meta-analysis of randomised controlled trials. BMJ. (2019) 364:l42. doi: 10.1136/bmj.l42

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Cho Y, Hong N, Kim K, Cho S, Lee M, Lee Y, et al. The effectiveness of intermittent fasting to reduce body mass index and glucose metabolism: a systematic review and meta-analysis. J Clin Med. (2019) 8:1645.

Google Scholar

23. Pellegrini M, Cioffi I, Evangelista A, Ponzo V, Goitre I, Ciccone G, et al. Effects of time-restricted feeding on body weight and metabolism. A systematic review and meta-analysis. Rev Endocr Metab Disord. (2020) 21:17–33.

Google Scholar

24. Engwall A. Development, Validation and Reliability of the Chrononutrition Profile. Fargo: NDSU Repository (2019).

Google Scholar

25. WHO. WHO Fact Sheet (2016). Obesity and overweight. Geneva: World Health Organization (2016).

Google Scholar

26. Papakonstantinou E, Oikonomou C, Nychas G, Dimitriadis G. Effects of diet, lifestyle, chrononutrition and alternative dietary interventions on postprandial glycemia and insulin resistance. Nutrients. (2022) 14:823. doi: 10.3390/nu14040823

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Charlot A, Hutt F, Sabatier E, Zoll J. Beneficial effects of early time-restricted feeding on metabolic diseases: importance of aligning food habits with the circadian clock. Nutrients. (2021) 13:1405. doi: 10.3390/nu13051405

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Mohd Azmi N, Juliana N, Azmani S, Mohd Effendy N, Abu I, Mohd Fahmi Teng N, et al. Cortisol on circadian rhythm and its effect on cardiovascular system. Int J Environ Res Public Health. (2021) 18:676.

Google Scholar

29. Fischer S, Meyer A, Hermann E, Tuch A, Munsch S. Night eating syndrome in young adults: Delineation from other eating disorders and clinical significance. Psychiatry Res. (2012) 200:494–501. doi: 10.1016/j.psychres.2012.07.028

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Yahia N, Brown C, Potter S, Szymanski H, Smith K, Pringle L, et al. Night eating syndrome and its association with weight status, physical activity, eating habits, smoking status, and sleep patterns among college students. Eat Weight Disord. (2017) 22:421–33. doi: 10.1007/s40519-017-0403-z

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Sevincer G, Ince E, Taymur I, Konuk N. Night eating syndrome frequency in university students: Association with impulsivity, depression, and anxiety. Klinik Psikofarmakoloji Bülteni-Bull Clin Psychopharmacol. (2016) 26:238–47.

Google Scholar

32. Yoshida J, Eguchi E, Nagaoka K, Ito T, Ogino K. Association of night eating habits with metabolic syndrome and its components: a longitudinal study. BMC Public Health. (2018) 18:1366. doi: 10.1186/s12889-018-6262-3

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Ishida Y, Yoshida D, Honda T, Hirakawa Y, Shibata M, Sakata S, et al. Influence of the accumulation of unhealthy eating habits on obesity in a general japanese population: The Hisayama study. Nutrients. (2020) 12:3160. doi: 10.3390/nu12103160

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Guentcheva I, Dugas E, Hanusaik N, Drapeau V, Sylvestre M, O’Loughlin J. Depression symptoms and night eating in young adulthood. Eat Weight Disord. (2020) 25:1593–600.

Google Scholar

35. Irfan M, Jabbar M, Hameed S. Dietary habits and prevalence of underweight/obesity in Students of University of Gujrat, Pakistan. J Liaquat Univ Med Health Sci. (2019) 18:175–80.

Google Scholar

36. Alkazemi D. Gender differences in weight status, dietary habits, and health attitudes among college students in Kuwait: A cross-sectional study. Nutr Health. (2019) 25:75–84. doi: 10.1177/0260106018817410

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Kinsey A, Eddy W, Madzima T, Panton L, Arciero P, Kim J, et al. Influence of night-time protein and carbohydrate intake on appetite and cardiometabolic risk in sedentary overweight and obese women. Br J Nutr. (2014) 112:320–7. doi: 10.1017/S0007114514001068

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Harris H, Eke A, Chavarro J, Missmer S. Fruit and vegetable consumption and risk of endometriosis. Hum Reprod. (2018) 33:715–27.

Google Scholar

39. Fontana R, Della Torre S. The deep correlation between energy metabolism and reproduction: a view on the effects of nutrition for women fertility. Nutrients. (2016) 8:87. doi: 10.3390/nu8020087

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Fujiwara T. Skipping breakfast is associated with dysmenorrhea in young women in Japan. Int J Food Sci Nutr. (2003) 54:505–9.

Google Scholar

41. Garcez M, de Castro M, Cesar C, Goldbaum M, Fisberg R. A chrononutrition perspective of diet quality and eating behaviors of Brazilian adolescents in associated with sleep duration. Chronobiol Int. (2021) 38:387–99. doi: 10.1080/07420528.2020.1851704

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Chaput J. Sleep patterns, diet quality and energy balance. Physiol Behav. (2014) 134:86–91.

Google Scholar

43. Kant A, Graubard B. Association of self-reported sleep duration with eating behaviors of American adults: NHANES 2005–2010. Am J Clin Nutr. (2014) 100:938–47. doi: 10.3945/ajcn.114.085191

PubMed Abstract | CrossRef Full Text | Google Scholar

44. de Castro M, Garcez M, Pereira J, Fisberg R. Eating behaviors and dietary intake associations with self-reported sleep duration of free-living Brazilian adults. Appetite. (2019) 137:207–17. doi: 10.1016/j.appet.2019.02.020

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Benham G. Stress and sleep in college students prior to and during the COVID-19 pandemic. Stress Health. (2021) 37:504–15.

Google Scholar

46. Das B, Evans E. Understanding weight management perceptions in first-year college students using the health belief model. J Am Coll Health. (2014) 62:488–97. doi: 10.1080/07448481.2014.923429

PubMed Abstract | CrossRef Full Text | Google Scholar

47. McArthur L, Pena M, Holbert D. Effects of socioeconomic status on the obesity knowledge of adolescents from six Latin American cities. Int J Obesity. (2001) 25:1262–8. doi: 10.1038/sj.ijo.0801674

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Sichert-Hellert W, Beghin L, De Henauw S, Grammatikaki E, Hallström L, Manios Y, et al. Nutritional knowledge in European adolescents: results from the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study. Public Health Nutr. (2011) 14:2083–91.

Google Scholar

49. Monge-Rojas R, Garita C, Sánchez M, Muñoz L. Barriers to and motivators for healthful eating as perceived by rural and urban Costa Rican adolescents. J Nutr Educ Behav. (2005) 37:33–40. doi: 10.1016/s1499-4046(06)60257-1

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Story M, Neumark-Sztainer D, French S. Individual and environmental influences on adolescent eating behaviors. J Am Diet Assoc. (2002) 102:S40–51.

Google Scholar

51. Neumark-Sztainer D, Story M, Ackard D, Moe J, Perry C. The “family meal”: views of adolescents. Journal of Nutrition Education. (2000) 32:329–34.

Google Scholar

52. Gillman M, Rifas-Shiman S, Frazier A, Rockett H, Camargo C Jr., Field A, et al. Family dinner and diet quality among older children and adolescents. Arch Fam Med. (2000) 9:235.

Google Scholar

53. Tavolacci M, Ladner J, Déchelotte P. Sharp increase in eating disorders among university students since the COVID-19 pandemic. Nutrients. (2021) 13:3415. doi: 10.3390/nu13103415

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Bernardo G, Jomori M, Fernandes A, Proença R. Food intake of university students. Rev Nutr. (2017) 30:847–65.

Google Scholar

55. Wy G, Mt M, Ms Z, As H. Differences in eating behaviors, dietary intake and body weight status between male and female Malaysian University students. Malays J Nutr. (2011) 17:213–28.

PubMed Abstract | Google Scholar