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Article

Examining the Relationship Between Increased Vegetable Consumption and Lifestyle Characteristics Among School-Aged Children: A Descriptive Study

by
Konstantinos D. Tambalis
1,*,
Dimitris Tampalis
2,
Demosthenes B. Panagiotakos
3 and
Labros S. Sidossis
4
1
Department of Physical Education and Sport Science, National and Kapodistrian University of Athens, 11528 Athens, Greece
2
Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
3
Department of Nutrition and Dietetics, School of Health Science & Education, Harokopio University, 17676 Athens, Greece
4
Department of Kinesiology and Health, Rutgers University, New Brunswick, NJ 08901, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(15), 8665; https://doi.org/10.3390/app15158665
Submission received: 21 June 2025 / Revised: 31 July 2025 / Accepted: 1 August 2025 / Published: 5 August 2025
(This article belongs to the Special Issue Potential Health Benefits of Fruits and Vegetables—4th Edition)
Versions Notes

Abstract

Featured Application

The availability and cost of fresh, high-quality vegetables should be addressed in public health initiatives.

Abstract

The purpose of this study was to examine vegetable consumption and its relationship with lifestyle characteristics among children and adolescents. Data from a health survey administered to a representative sample of 177,091 schoolchildren between the ages of 8 and 17 were employed in this observational, cross-sectional investigation. Physical activity level, screen time, and sleeping patterns were assessed using self-completed questionnaires. Vegetable consumption and dietary habits were analyzed using the Mediterranean Diet Quality Index for Children and Adolescents. Participants consuming vegetables more than once daily were categorized as consumers vs. non-consumers. Physical education teachers measured anthropometric and physical fitness factors. Descriptive statistics and binary logistic regression analysis were conducted, and the odds ratio with the corresponding 95% confidence interval was calculated and adjusted for confounders. Vegetables were consumed once or more times a day by more females than males (25.5% vs. 24.0%, p < 0.001). In both sexes, vegetable consumers slept more, ate healthier, spent less time on screens, and had better anthropometric and aerobic fitness measurements than non-consumers. Healthy eating practices, such as regularly consuming fruits, legumes, nuts, and dairy products, were strongly correlated with vegetable intake. For every one-year increase in age, the odds of being a vegetable consumer decreased by 8% and 10% in boys and girls, respectively. Overweight/obese participants had lower odds of being a vegetable consumer by 20%. Increased screen time, inadequate physical activity, and insufficient sleeping hours decreased the odds of being a vegetable consumer by 22%, 30%, and 25%, respectively (all p-values < 0.001). Overall, a healthier lifestyle profile was associated with higher vegetable intake for both sexes among children and adolescents.

1. Introduction

Since healthy eating habits frequently persist throughout adulthood, it is crucial to be established during childhood [1,2]. Fruit and vegetable consumption tends to follow children’s eating patterns in later life, highlighting the need to promote its frequent intake at a young age [1,2,3]. Vegetables are a great source of vitamins, minerals, fiber, and other nutrients that are vital for healthy growth and development as well as the prevention of disease [4]. Consuming more vegetables is associated with a lower risk of heart disease, stroke, cancer, and high blood pressure [5]. Additionally, eating vegetables was connected to better gut health and increased psychological well-being [5]. Conversely, poor dietary practices, such as a diet deficient in fruits and vegetables, can result in the development of diabetes, obesity, cardiovascular disease, some types of cancer, and weakened immunity [6]. According to the World Health Organization (WHO),a minimum of 400 g of fruits and vegetables should be consumed daily by children and adolescents [4]. To meet their nutritional demands for optimal growth and development, it was also suggested that they must consume a variety of vegetables [4]. While several nations have their vegetable dietary guidelines, all of them emphasise the need to include enough vegetables in children’s diets [4,7]. However, in most Western nations, schoolchildren consume significantly less than the recommended amount [8,9,10,11]. The key factors that mainly affect their consumption are sex, age, socioeconomic position, tastes, parental intake, and home availability/accessibility [12].
Vegetable eating at the early stages of life was associated with lifestyle and sociodemographic characteristics. Age seems to be one of them, as boys and older children often consume more vegetables than girls and younger children [12,13]. Another factor that is related to its consumption is a sedentary lifestyle. The extended use of computers or television might lead to a disregard for eating fruits and vegetables [14], whereas spending more time on screens was linked to consuming fewer vegetables [15,16,17]. Nevertheless, a review study showed a moderate relationship between screen time and school-aged children’s lower intake of fruits and vegetables [18]. Conversely, it proposed that people who consume more vegetables are more physically active and fit [17,18,19,20,21]. Regarding its potential association with sleep, both short and long sleep durations are associated with decreased vegetable intake compared to the recommended hours [22,23]. Vegetable consumption is also related to childhood obesity [24,25]. It proposed that the lack of vegetable markets could deter children from choosing nutritious foods, which might contribute to it [24]. Moreover, a review study showed that youngsters who eat fewer vegetables are more likely to be overweight, while it is speculated that children who eat more vegetables are less likely to be obese and have lower BMIs [25].
Accordingly, there is evidence that vegetable intake is connected to student sedentary behavior, physical activity (PA), and physical fitness (PF) [14,15,16,17,18,19,20,21,22,23,24,25]. However, most studies have looked at each factor separately, with limited age groups and small sample sizes [14,15,16,17,18,19,20,21,22,23,24,25]. Analytically, researchers have explored the relationship between eating vegetables and sleep [7,22,23], sedentary behavior [15,16,17,18], PA [16,17,18], and obesity [24,25], in specific age groups [14,15,16,17,23]. Moreover, no nationally representative survey has looked at the prevalence of vegetable consumption among students or the factors linked to it [14,15,16,17,18,23]. Therefore, by providing descriptive data about the association between increased vegetable consumption and several factors in an extended, representative sample of students, the study could contribute to the scientific literature. As a result, health initiatives that attempt to increase their consumption can be informed by this understanding.
The specific objectives of this study were to record the prevalence of vegetable intake and investigate the relationship between higher vegetable consumption and lifestyle characteristics, considering several potential confounders.

2. Materials and Methods

2.1. Participants

For this study, data from a sizable, nationally representative sample of Greek children and adolescents aged 8 to 17 were used. The study’s population-representative data originated from a nationwide school-based health survey conducted in Greece under the Ministry of Education’s auspices. Data on anthropometry, diet, PA, screen time, PF, age, and sex were collected from March to May 2018. The survey included 177,091 students or more than 40% of the entire population, from elementary (aged 8 to 12) and middle (aged 13 to 17) public and private institutions. Of these people, 51% were male and 49% female. This study was performed in line with the principles of the Declaration of Helsinki, Finland. Parents signed an informed consent form enabling their children to participate in the national health investigation after receiving written notification. Moreover, verbal informed consent for the child to participate in the measurements was taken from their teachers. The study was carried out in line with the STROBE guidelines for observational studies.

2.2. Assessment of Demographic and Anthropometric Data

The school principals provided demographic information about the students, including their class, school, sex, and birth date. A standardized method was employed to determine the participant’s height, body weight, and waist circumference. The International Obesity Task Force’s age- and sex-specific BMI cut-off criteria were used to classify the children’s BMI status as normal or overweight/obese [26]. Central obesity was defined as having a waist circumference-to-height ratio (WHtR) of 0.5 or higher [27]. Anthropometric measurements were performed by physical education (PE) specialists.

2.3. Assessment of Physical Fitness Levels

The Euro-fit PF test battery was used to determine the children’s PF levels [28]. The battery included five tests: a standing long jump to evaluate lower body explosive power; a multi-stage 20 m shuttle run test (20 m SRT) to estimate aerobic performance; a sit-up test in 30 s to evaluate abdominal and hip-flexor muscle endurance; an assessment of flexibility using the sit-and-reach test; and a maximum 10 × 5 m shuttle run test to evaluate speed and agility. This study only used data from the 20 m SRT. The PE professionals who performed the fitness evaluations were trained using an extensive operating handbook. Standardization of the measurement procedure decreased inter-rater variability among schools.

2.4. Assessment of Dietary Habits

The participating children completed an electronic questionnaire at school with the assistance of their teachers, recording their sedentary, PA, and food behaviors. The Mediterranean Diet Quality Index for Children and Adolescents (KIDMED index) was used to assess the students’ eating patterns [29]. This score, which consists of 16 yes/no questions, evaluates adherence to the fundamentals of the Mediterranean diet (MD) and nutritional guidelines for youth. A value of +1 is assigned to positive responses, whereas a value of −1 is assigned to negative responses. The total KIDMED score, which goes from 0 to 12, is divided into three levels: ≥8, which denotes optimal compliance to the MD (sufficient dietary habits); ≤7, which denotes average compliance to the MD and a need for improvement to meet diet guidelines (relatively sufficient dietary habits); and ≤3, which denotes little compliance to the MD and low diet quality (insufficient dietary habits). The main outcome of this study was increased vegetable consumption, as indicated by whether participants consumed more than one serving of raw or cooked vegetables daily. Those who did not consume vegetables regularly were classified as “non-consumers”. Vegetable consumption was measured using the question, “Do you eat fresh or cooked vegetables more than once a day?” Responses were then combined for analysis.

2.5. Assessment of Self-Reported Physical Activity and Sedentary Time

Self-reported PA and inactive time were measured using a validated questionnaire that included closed-ended questions regarding the quantity, duration, and level of children’s involvement in organized sports, school, and recreational PA [30]. By multiplying the reported activities by the minutes of moderate-to-intense physical activity (MVPA) and dividing the result by seven, the mean daily time spent by children in MVPA was determined. The quantity of time spent sedentary was calculated by multiplying the weekly frequency and duration of each bout of sedentary behavior, then dividing the result by seven. Students were classified as sedentary if they spent more than two hours a day on screens, which are a stand-in for sedentary activities [31]. Measurements of sleep duration were made using self-reported data. The criteria for adequate sleep were deemed to be met by adolescents and children sleeping not less than eight and nine hours every day, respectively [32].

2.6. Statistical Analysis

The descriptive data were shown as mean ± standard deviation or frequency (percentages). The chi-square test was used to examine associations between categorical variables, while the Student’s t-test was employed to evaluate differences in the mean values of normally distributed data. To evaluate the potential impact of various dietary practices on increased vegetable consumption (yes vs. no), binary logistic regression analysis was used. Confounders were considered while calculating the odds ratio (OR) and related 95% confidence interval (CI). Moreover, the possible impact of a number of demographic and lifestyle factors on the frequency of vegetable consumption were also evaluated using binary logistic regression analysis. OR with a corresponding 95% CI was calculated to provide an adjusted association of variables while accounting for confounding. The model’s goodness-of-fit was assessed using Hosmer and Lemeshow's goodness-of-fit test, and outliers and significant observations were found using residual analysis utilizing the dbeta, leverage, and Cook’s distance D statistics. Discriminant analysis was utilized to examine the strength of each component in the outcome. The SPSS version 23.0 software for Windows (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. A p-value of less than 0.05 was established as the threshold for statistical significance in two-sided hypotheses.

3. Results

A total of 177,091 kids and teenagers between the ages of 8 and 17 participated in the survey. Table 1 displays the basic descriptive information for the entire sample, categorized by sex. In addition to having higher KIDMED scores, more girls than boys reported eating vegetables more than once a day (25.5% vs. 24.0%, p < 0.001). Anthropometric, PA, screen time, and aerobic fitness metrics were all higher in boys than in girls (all p-values < 0.001). Table 2 presents the anthropometric and behavioral characteristics of the study participants based on how frequently they eat vegetables. Both sexes who were categorized as frequent vegetable consumers had superior anthropometric measurements, reduced screen time, healthier food habits, better aerobic fitness, and slept more than non-consumers of the same sex (all p-values < 0.01).
Next, we apply logistic regression analysis to evaluate the potential impact of dietary practices on increasing vegetable consumption. The unadjusted model’s results (Table 3, Model 1) revealed that eating frequently fruits, nuts, pulses, pasta or rice, and two yogurts and/or cheese (40 g) per day increased the likelihood that both sexes would consume vegetables more than once a day. Additionally, both sexes were less likely to consume vegetables if they often consumed sweets. Regardless of adjusting for waist circumference, BMI, and age, there was still a significant correlation between eating habits and vegetable intake (Table 3, Model 2). Further adjustments for screen and sleep time, and PA did not significantly alter the results (Table 3, Model 3).
Based on previous findings (Table 2) that show vegetable consumers had a better lifestyle profile than non-consumers, stepwise logistic regression analyses (four Models) were conducted to investigate potential associations between participant characteristics and vegetable intake (>1 per day vs. none) in both sexes.
The results showed (Table 4, Model 1) that the likelihood of eating vegetables decreased for each year of increasing age, by 10% and 8% for girls and boys, respectively. Additionally, central fatness and overweight/obesity decreased the likelihood of eating vegetables by roughly 12% and 20%, respectively, for both sexes. After further adjusting for the KIDMED scores, the influence of overweight status and age wasunaltered but eating habits increased the likelihood of eating vegetables by 3.6 times (Table 4, Model 2). After adjusting for screen time and sleep duration, it was found that both excessive screen time and inadequate sleep reduced the probability of vegetableintake by almost 18% (Table 4, Model 3). When PA levels and aerobic fitness measurements were included in the analysis, the effects of the previous components remained (Table 4, Model 4). Moreover, the findings revealed that increased aerobic performance was associated with higher odds of eating vegetables, while low PA levels reduced the likelihood of eating vegetables by 30%. Discriminant analysis was employed to ascertain whether the predictors could more successfully distinguish between consumers and non-consumers. Standardised function coefficients (which show how heavily each variable is weighted to maximise group discrimination) showed that dietary habits (0.91), PA (0.44), and age (−0.29) were the more significant factors to distinguish vegetable consumers fromnon-consumers, for both sexes. This model predicted correctly84% of consumers and 68% of non-consumers (i.e., how well the model predicts the group membership).

4. Discussion

As far as we know, this study is among the few thatexamined several lifestyle and anthropometric correlations of vegetable consumption in a country-representative population. We have included data from 177,091 children (ages 8 to 17) to provide consistent, standardised, and comparable results. The following are the most significant findings: (a) Almost one in four schoolchildren ate more than one vegetable each day; (b) participants consuming vegetables presented lower obesity rates and better aerobic fitness; and (c) consuming more vegetables was strongly associated with longer sleep duration, more PA, and healthy eating practices, as well as decreased screen time.
The first noteworthy finding was that the participants who consumed more than one vegetable daily wereonly 25%. Data from a national survey in the USA (2015–2018) showed that almost 90% of the participants (aged 2 to19 years) ate vegetables every day [33]. Other data among adolescents in North America, Europe, and Oceania proposed that the mean vegetable and fruit intake was significantly lower than the recommendation (400 g/daily) [34]. Conclusively, it seems that vegetable intake varies significantly between and within countries [35].
Another important point was the significant decrease in the age effect of increased vegetable consumption, from 27.6% at age 8 to 18.8% at age 17. These findings are like a study from the United Kingdom, which stated that vegetable intake changed between 7 and 12 years, in both sexes (our data indicated a reduction from 27.6% at age 8 to 25.4% at age 12) [36]. Additionally, findings from the Centers for Disease Control and Prevention (CDC) stated that the proportion of participants who ate vegetables every day declined with age (CDC) [37]. These findings are most likely explained by the independence of older participants in vegetable intake than younger ones, as the likelihood of youngsters being influenced by their parents is higher.
In our study, higher vegetable intake was substantially linked to lower chances for overweight/obesity and central obesity by about 20% and 12%, respectively, in both sexes. Related research concluded that an adequate vegetable diet was related to a decreased chance of childhood obesity [24,25]. Similarly, being overweight was related to a reduced vegetable intake [24,25]. In general, the WHO recommendations encourage children to eat a diet rich in fruits and vegetables, as it could postpone or possibly prevent childhood obesity [38].
Based on the current findings, vegetable consumers were almost 3.6 times more likely to have adequate eating habits. It is well known that eating more vegetables is inversely correlated with unhealthy eating habits like consuming sweets frequently [30]. The WHO and national dietary guidelines advise children to prefer healthier food choices and to eat enough vegetables as they are rich in vital nutrients and may help prevent several non-communicable diseases [4,7,24,38].
We also incorporated a substantial correlation between increasing vegetable consumption and screen time. Specifically, those who spent more time on screens (>2 h per day) were about 20% less likelyto eat vegetables, regardless of sex. Screen usage and healthy eating habits were found to be negatively correlated in a review of research [39]. In addition, another review revealed an inverse association between screen time, particularly television viewing, and schoolchildren’s lower intake of fruits and vegetables [18]. Conclusively, there is evidence that screen time has a negative correlation with children’s and adolescents’ consumption of fruits and vegetables [39,40].
The available data suggested that both sexes had a 19% reduced probability of eating vegetables if they slept for less than 8 to 9 h each day. In line with our results, studies among children showed that shorter nap times were linked to more frequent consumption of fruits and vegetables [22,23]. Also, another study in 13,879 young adolescents speculated that eating adequate fruits and vegetables was linked to fewer sleep issues [41]. Moreover, review studies stated that eating enough fruits and vegetables was linked to higher-quality sleep and a strong correlation between inadequate sleep and poorer diets among schoolchildren [42,43].
Our results revealed that lower PA levels reduced the likelihood of consuming vegetables by nearly 30%. A similar study showed that girls whose parents reported strong PA modeling were more likely to consume >/=5 servings of fruits and vegetables per day [21]. Also, the current findings are consistent with results from 9842 participants (6–17 years), which suggested that both boys and girls who had high levels of PA consumed more juice, fruits, and vegetables [19]. Similarly, another study revealed that frequent vegetable intake was associated withincreased chances of reaching PA guidelines among children and adolescents [17].
Finally, the current findings revealed that the higher the aerobic performance, the increased the chances of eating vegetables, although the odds ratio is not great (each lap higher in the 20 m shuttle run test increasesthe probabilities of being a vegetable consumer by 1.5%). In line with the above results, other studies incorporated a positive relationship between dietary choices and PF, and that daily vegetable consumption was linked to elevated odds of healthy aerobic fitness [44,45]. This association must be confirmed by another research. Obviously, a healthy lifestyle isassociatedwith better aerobic fitness, and these children were ableto choose healthy eating practices, as consuming more vegetables.
It is quite concerning that public health initiatives should address issues in light of the current evidence related to the availability and cost of fresh, high-quality vegetables in addition to deficiencies in nutrition knowledge [46,47].
Among the strengths of the current study was that it examined several variables and was conducted throughout a wide age range (8–17 years old). Furthermore, since primary and secondary education aremandatory in Greece, a sizable portion of the country’s childpopulation was investigated. As a result, its findings can be compared to those of other comparable and representative research.

Limitations

Methodological problems and the failure to evaluate potential confounding variables, such as socioeconomic status (SES), parental intake, and vegetable availability, which were likely linked to vegetable intake, are some of the study’s limitations. Analytically, it seems that SES plays a vital role in children’s healthy dietary habits as higher family SES is favorably linked with vegetable consumption [48,49]. Additionally, there is a higher likelihood that children of parents who consume an adequate daily quantity of vegetables will also follow healthy dietary habits [50]. Moreover, the fact that home availability of vegetables was not recorded constitutes another potential limitation, as it is considered to positively associate with children’s consumption of vegetables [51]. This study’s cross-sectional methodology, which makes it impossible to prove causation, is another potential limitation. Because the information on PA, sedentary time, sleep patterns, and eating habits was self-reported, social desirability bias could affect it. However, because responses were anonymous, participants had little incentive to underreport. Lastly, statistical significance is easily attained due to the huge sample size, and as a result, it candetect trivial differences. Consequently, future research, such as longitudinal studies to examine temporal relationships, intervention studies to promote vegetable consumption, or qualitative research, could be applied to better understand barriers and facilitators of vegetable consumption among schoolchildren.

5. Conclusions

The findings of this nationwide, representative health research showed thatonly one in four Greek children and adolescents eat more than one vegetable daily, while the frequency of vegetable consumption decreases with age. Vegetable-eating participants were nearly four times more likelyto present sufficient dietary behavior. Better aerobic fitness and a decreased risk of central and total obesity were also associated with increased vegetable intake. Higher screen time, inadequate physical activity, and insufficient sleeping hours decreased the odds of being a vegetable consumer. Healthy vegetable consumption habits should be formed early in life, with emphasis on averting negative health consequences later.

Author Contributions

Conceptualization, K.D.T. and D.B.P.; methodology, D.B.P.; software, D.B.P.; validation, D.B.P., D.T. and L.S.S.; formal analysis, K.D.T. and D.T.; investigation, L.S.S. and K.D.T.; resources, D.T.; data curation, L.S.S.; writing—original draft preparation, K.D.T.; writing—review and editing, D.T., D.B.P. and L.S.S.; visualization, D.T.; supervision, K.D.T.; project administration, L.S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethical Review Board of the Ministry of Education and the Ethical Review Committee of Harokopio University (decision No 37/20-02-2013).

Informed Consent Statement

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

Data Availability Statement

Data described in the manuscript will not be made available because it contains personal data and is under the supervision of the Ministry of Education of the country.

Acknowledgments

This study was supported by the Hellenic Ministry of Education and Religious Affairs, and the Department of Nutrition and Dietetics Graduate Program, Harokopio University of Athens.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
WHOWorld Health Organization
PAPhysical Activity
PFPhysical fitness
PEPhysical Education
MDMediterranean diet
OROdds ratio
CIConfidence interval
CDCCenter for Disease Control and Prevention
SESSocio-economic status

References

  1. Horovitz, O. Theory of Food: Unravelling the Lifelong Impact of Childhood Dietary Habits on Adult Food Preferences across Different Diet Groups. Nutrients 2024, 16, 428. [Google Scholar] [CrossRef] [PubMed]
  2. Appannah, G.; Murray, K.; Trapp, G.; Dymock, M.; Oddy, W.H.; Ambrosini, G.L. Dietary pattern trajectories across adolescence and early adulthood and their associations with childhood and parental factors. Am. J. Clin. Nutr. 2021, 113, 36–46. [Google Scholar] [CrossRef] [PubMed]
  3. Małachowska, A.; Jeżewska-Zychowicz, M. Does Examining the Childhood Food Experiences Help to Better Understand Food Choices in Adulthood? Nutrients 2021, 13, 983. [Google Scholar] [CrossRef] [PubMed]
  4. World Health Organisation European Region. Food Based Dietary Guidelines in the WHO European Region; WHO Europe: Copenhagen, Denmark, 2003. [Google Scholar]
  5. Zhang, Y.; Tabung, F.K.; Smith-Warner, S.A.; Giovannucci, E. High-quality fruit and vegetable characterized by cardiometabolic biomarkers and its relation to major chronic disease risk: Results from 3 prospective United States cohort studies. Am. J. Clin. Nutr. 2024, 120, 369–379. [Google Scholar] [CrossRef]
  6. Schroder, H. Protective mechanisms of the Mediterranean diet in obesity and type 2 diabetes. J. Nutr. Biochem. 2007, 18, 149–160. [Google Scholar] [CrossRef]
  7. U.S. Department of Health and Human Services, U.S. Department of Agriculture. 2020–2025 Dietary Guidelines for Americans, ed. 9(ed2020); 2020. Available online: https://www.dietaryguidelines.gov/resources/2020-2025-dietary-guidelines-online-materials (accessed on 10 May 2025).
  8. Krebs-Smith, S.M.; Cook, A.; Subar, A.F.; Cleveland, L.; Friday, J.; Hahle, L.L. Fruit and vegetable intakes of children and adolescents in the United States. Arch. Pediatr. Adolescs Med. 1996, 150, 81–86. [Google Scholar] [CrossRef]
  9. Munoz, K.A.; Krebs-Smith, S.M.; Ballard-Barbash, R.; Cleveland, L.E. Food intakes of US children and adolescents compared with recommendations. Pediatrics 1997, 100, 323–329. [Google Scholar] [CrossRef]
  10. Currie, C.; Robert, C.; Morgan, A.; Smith, R.; Settertobulte, W.; Samdal, O.; Rasmussen, V.B. (Eds.) Young People’s Health in Context. Health Behaviour in School-Aged Children (HBSC): International Report from the 2001/2002 Survey; Health Policy Series: Health Policy for Children and Adolescents Issue 4; WHO Regional Office for Europe: Copenhagen, Denmark, 2004. [Google Scholar]
  11. Yngve, A.; Wolf, A.; Poortvliet, E.; Elmadfa, I.; Brug, J.; Ehrenblad, B.; Franchini, B.; Haraldsdóttir, J.; Krølner, R.; Maes, L.; et al. Fruit and vegetable intake in a sample of 11-year-old children in 9 European countries: The Pro Children Cross-Sectional Survey. Ann. Nutr. Metab. 2005, 49, 236–245. [Google Scholar] [CrossRef]
  12. Rasmussen, M.; Krølner, R.; Klepp, K.I. Determinants of fruit and vegetable consumption among children and adolescents: A review of the literature. Part I: Quantitative studies. Int. J. Behav. Nutr. Phys. Act. 2006, 3, 22. [Google Scholar] [CrossRef]
  13. Health Survey for England. Children Aged 5 to 15 Who Eat Five or more Portions of Fruit and Vegetables per Day, by Age. 2016. Available online: https://digital.nhs.uk/data-and-information/publications/statistical/health-survey-for-england/2022-part-1/childrens-health-and-health-related-behaviours#:~:text=Children's%20fruit%20and%20vegetable%20consumption%2C%20by%20survey,similar%20for%20boys%20 (accessed on 5 June 2025).
  14. Nicklas, T.A.; Johnson, C.C.; Farris, R.; Rice, R.; Lyon, L.; Shi, R. Development of a school-based nutrition intervention for high school students: Gimme 5. Am. J. Health Promot. 1997, 8, 315–322. [Google Scholar] [CrossRef]
  15. Huo, J.; Kuang, X.; Xi, Y.; Xiang, C.; Yong, C.; Liang, J.; Zou, H.; Lin, Q. Screen Time and Its Association with Vegetables, Fruits, Snacks and Sugary Sweetened Beverages Intake among Chinese Preschool Children in Changsha, Hunan Province: A Cross-Sectional Study. Nutrients 2022, 14, 4086. [Google Scholar] [CrossRef] [PubMed]
  16. Sauser, K.; Morgan, E. Does increased screen time contribute to adverse dietary choices in children? Evid.-Based Pract. 2022, 25, 30–31. [Google Scholar] [CrossRef]
  17. Sampasa-Kanyinga, H.; Hamilton, H.A.; Chaput, J.P. Movement behaviours, breakfast consumption, and fruit and vegetable intake among adolescents. JASSB 2022, 1, 4. [Google Scholar] [CrossRef] [PubMed]
  18. Stiglic, N.; Viner, R.M. Effects of screen time on the health and well-being of children and adolescents: A systematic review of reviews. BMJ Open 2019, 9, e023191. [Google Scholar] [CrossRef]
  19. Manz, K.; Mensink, G.B.M.; Finger, J.D.; Haftenberger, M.; Brettschneider, A.-K.; Barbosa, C.L.; Krug, S.; Schienkiewitz, A. Associations between Physical Activity and Food Intake among Children and Adolescents: Results of KiGGS Wave 2. Nutrients 2019, 11, 1060. [Google Scholar] [CrossRef]
  20. Tambalis, K.D.; Panagiotakos, D.B.; Psarra, G.; Sidossis, L.S. Concomitant Associations between Lifestyle Characteristics and Physical Activity Status in Children and Adolescents. J. Res. Health Sci. 2019, 19, e00439. [Google Scholar]
  21. Buchanan, L.R.; Wethington, H.R.; Finnie, R.K.C.; Mercer, S.L.; Merlo, C.; Michael, S.; Sliwa, S.; Pratt, C.A.; Ochiai, E.; The Community Preventive Services Task Force. A Community Guide Systematic Review: School Dietary and Physical Activity Interventions. Am. J. Prev. Med. 2023, 64, 441–451. [Google Scholar] [CrossRef]
  22. Thapa, A.; Lahti, T.; Maukonen, M.; Partonen, T. Consumption of fruits and vegetables and its association with sleep duration among Finnish adult population: A nationwide cross-sectional study. Front. Nutr. 2024, 11, 1319821. [Google Scholar] [CrossRef]
  23. Holmes, J.F.; St Laurent, C.W.; Spencer, R.M.C. Unhealthy Diet Is Associated with Poor Sleep in Preschool-Aged Children. J. Genet. Psychol. 2021, 182, 289–303. [Google Scholar] [CrossRef]
  24. Yang, S.; Zhang, X.; Feng, P.; Wu, T.; Tian, R.; Zhang, D.; Zhao, L.; Xiao, C.; Zhou, Z.; He, F.; et al. Access to fruit and vegetable markets and childhood obesity: A systematic review. Obes. Rev. 2021, 22 (Suppl. 1), e12980. [Google Scholar] [CrossRef]
  25. Jakobsen, D.D.; Brader, L.; Bruun, J.M. Association between Food, Beverages and Overweight/Obesity in Children and Adolescents—A Systematic Review and Meta-Analysis of Observational Studies. Nutrients 2023, 15, 764. [Google Scholar] [CrossRef] [PubMed]
  26. Cole, T.; Bellizzi, M.; Flegal, K.; Dietz, W.H. Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ 2000, 320, 1240–1243. [Google Scholar] [CrossRef] [PubMed]
  27. Browning, L.M.; Hsieh, S.D.; Ashwell, M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr. Res. Rev. 2010, 23, 247–269. [Google Scholar] [CrossRef] [PubMed]
  28. Research CoECoEoS. Eurofit: Handbook for the Eurofit Tests of Physical Fitness; Council of Europe: Strasbourg, France, 1993; pp. 1–75. [Google Scholar]
  29. Serra-Majem, L.; Ribas, L.; Ngo, J.; Ortega, R.M.; García, A.; Pérez-Rodrigo, C.; Aranceta, J. Food, youth and the Mediterranean diet in Spain. Development of KIDMED, Mediterranean Diet Quality Index in children and adolescents. Public Health Nutr. 2004, 7, 931–935. [Google Scholar] [CrossRef] [PubMed]
  30. Grigorakis, D.A.; Georgoulis, M.; Psarra, G.; Tambalis, K.D.; Panagiotakos, D.B.; Sidossis, L.S. Prevalence and lifestyle determinants of central obesity in children. Eur. J. Nutr. 2016, 55, 1923–1931. [Google Scholar] [CrossRef] [PubMed]
  31. Colley, R.C.; Wong, S.L.; Garriguet, D.; Janssen, I.; Connor Gorber, S.; Tremblay, M.S. Physical activity, sedentary behaviour and sleep in Canadian children: Parent-report versus direct measures and relative associations with health risk. Health Rep. 2012, 23, 45–52. [Google Scholar]
  32. Paruthi, S.; Brooks, L.J.; D'AMbrosio, C.; Hall, W.A.; Kotagal, S.; Lloyd, R.M.; Malow, B.A.; Maski, K.; Nichols, C.; Quan, S.F.; et al. Consensus Statement of the American Academy of Sleep Medicine on the Recommended Amount of Sleep for Healthy Children: Methodology and Discussion. J. Clin. Sleep Med. 2016, 12, 1549–1561. [Google Scholar] [CrossRef]
  33. Wambogo, E.A.; Ansai, N.; Ahulwalia, N.; Ogden, C.L. Fruit and Vegetable Consumption Among Children and Adolescents in the United States, 2015–2018. NCHS Data Brief 2020, 391, 1–8. [Google Scholar]
  34. Rosi, A.; Paolella, G.; Biasini, B.; Scazzina, F.; SINU Working Group on Nutritional Surveillance in Adolescents. Dietary habits of adolescents living in North America, Europe or Oceania: A review on fruit, vegetable and legume consumption, sodium intake, and adherence to the Mediterranean Diet. Nutr. Metab. Cardiovasc. Dis. 2019, 29, 544–560. [Google Scholar] [CrossRef]
  35. European Union. Review of Current Practices to Increase the Intake of Fruit and Vegetables. 2015. Available online: https://health.ec.europa.eu/system/files/2016-11/2015_myhealthyfamily_literaturereview_en_0.pdf (accessed on 10 May 2025).
  36. Albani, V.; Butler, L.T.; Traill, W.B.; Kennedy, O.B. Fruit and vegetable intake: Change with age across childhood and adolescence. Br. J. Nutr. 2017, 117, 759–765. [Google Scholar] [CrossRef]
  37. Wallace, T.C.; Bailey, R.L.; Blumberg, J.B.; Burton-Freeman, B.; Chen, C.-Y.O.; Crowe-White, K.M.; Drewnowski, A.; Hooshmand, S.; Johnson, E.; Lewis, R.; et al. Fruits, vegetables, and health: A comprehensive narrative, umbrella review of the science and recommendations for enhanced public policy to improve intake. Crit. Rev. Food Sci. Nutr. 2020, 60, 2174–2211. [Google Scholar] [CrossRef] [PubMed]
  38. World Health Organization. Increasing Fruit and Vegetable Consumption to Reduce the risk of Noncommunicable Diseases. 2023. Available online: https://www.who.int/tools/elena/interventions/fruit-vegetables-ncds (accessed on 28 May 2025).
  39. Costigan, S.A.; Barnett, L.; Plotnikoff, R.C. The health indicators associated with screen-based sedentary behavior among adolescent girls: A systematic review. J. Adolesc. Health 2013, 52, 382–392. [Google Scholar] [CrossRef]
  40. Pearson, N.; Biddle, S.J. Sedentary behavior and dietary intake in children, adolescents, and adults. A systematic review. Am. J. Prev. Med. 2011, 41, 178–188. [Google Scholar] [CrossRef] [PubMed]
  41. Zhao, Y.; Qu, D.; Liang, K.; Bao, R.; Chen, S. Eating habits matter for sleep difficulties in children and adolescents: A cross-sectional study. Front. Pediatr. 2023, 11, 1108031. [Google Scholar] [CrossRef] [PubMed]
  42. Amat-Camposo, R.; Riquelme-Gallego, B.; Soto-Méndez, M.J.; Hernández-Ruiz, Á. Relación entre la dieta, aspectosnutricionales y la calidad del sueñoen población pediátrica (Relationship between diet, nutritional aspects and sleep quality in a pediatric population). Nutricionhospitalaria 2023, 41, 462–476. [Google Scholar] [CrossRef]
  43. Dashti, H.S.; Scheer, F.A.; Jacques, P.F.; Lamon-Fava, S.; Ordovás, J.M. Short sleep duration and dietary intake: Epidemiologic evidence, mechanisms, and health implications. Adv. Nutr. 2015, 6, 648–659. [Google Scholar] [CrossRef] [PubMed]
  44. Tambalis, K.D.; Panagiotakos, D.B.; Psarra, G.; Sidossis, L.S. Association of cardiorespiratory fitness levels with dietary habits and lifestyle factors in schoolchildren. Appl. Physiol. Nutr. Metab. 2019, 44, 539–545. [Google Scholar] [CrossRef]
  45. David, T.; Julien, A.; Laurie, I.; Nordine, L.; Sébastien, R.; Eric, D.; Martine, M.; Pascale, D. Are eating habits associated with physical fitness in primary school children? Eat. Behav. 2013, 14, 83–86. [Google Scholar] [CrossRef]
  46. Deroover, K.; Bucher, T.; Vandelanotte, C.; de Vries, H.; Duncan, M.J. Practical nutrition knowledge mediates the relationship between sociodemographic characteristics and diet quality in adults: A cross-sectional analysis. Am. J. Health Promot. 2019, 34, 59–62. [Google Scholar] [CrossRef]
  47. McKinnon, L.; Giskes, K.; Turrell, G. The contribution of three components of nutrition knowledge to socio-economic differences in food purchasing choices. Public Health Nutr. 2014, 17, 1814–1824. [Google Scholar] [CrossRef]
  48. Boelens, M.; Raat, H.; Wijtzes, A.I.; Schouten, G.M.; Windhorst, D.A.; Jansen, W. Associations of socioeconomic status indicators and migrant status with risk of a low vegetable and fruit consumption in children. SSM-Popul. Health 2022, 17, 2352–8273. [Google Scholar] [CrossRef]
  49. Hinnig, P.; de Monteiro, F.; de Assis, J.S.; Levy, M.A.A.; Peres, R.B.; Perazi, M.A. Dietary patterns of children and adolescents from high, medium and low human development countries and associated socioeconomic factors: A systematic review. Nutrients 2018, 10, 436. [Google Scholar] [CrossRef]
  50. Coto, J.; Pulgaron, E.R.; Graziano, P.A.; Bagner, D.M.; Villa, M.; Malik, J.A.; Delamater, A.M. Parents as Role Models: Associations between parent and young children’s weight, Dietary Intake, and physical activity in a minority sample. Matern. Child Health J. 2019, 23, 943–950. [Google Scholar] [CrossRef]
  51. Serasinghe, N.; Vepsäläinen, H.; Lehto, R.; Abdollahi, A.M.; Erkkola, M.; Roos, E.; Ray, C. Associations between socioeconomic status, home food availability, parental role-modeling, and children’s fruit and vegetable consumption: A mediation analysis. BMC Public Health 2023, 23, 1037. [Google Scholar] [CrossRef]
Table 1. Study participants’ baseline characteristics (means, SD).
Table 1. Study participants’ baseline characteristics (means, SD).
TotalBoysGirlsp-Value *
Age (years) 9.88 (2.8)9.91 (2.8)9.84 (2.8)<0.001
Children 8–11 years, n (%)100,13451,161 (50.9)48,973 (49.1)<0.001
Adolescents 12–17 years, n (%)76,97539,660 (52.5)37,315 (47.5)<0.001
Height (cm)149 (13.5)150 (14.5)148 (12.3)<0.001
Weight (kg)44.5 (14.2)45.5 (15.2)43.5 (12.9)<0.001
Body mass index (kg/m2)19.7 (3.8)19.8 (3.8)19.5 (3.7)<0.001
Waist circumference (cm)70.4 (10.7)71.6 (11.1)69.2 (10.2)<0.001
Waist-to-height ratio0.30 (0.46)0.32 (0.47)0.28 (0.45)<0.001
KIDMED score (0, 12) †6.7 (2.4)6.7 (2.4)6.8 (2.4)<0.001
Vegetable intake > 1/d, n (%)42,310 (24.7)21,051 (24.0)21,259 (25.5)<0.001
Physical activity (h/wk)9.4 (5.5)10.4 (5.9)8.4 (5.2)<0.001
Screen time (h/wk)8.6 (8.5)9.3 (8.8)7.8 (7.8)<0.001
Sleep time weekdays, (h/d)8.6 (1.6)8.6 (1.6)8.7 (1.6)<0.001
20 m shuttle run (laps)31.1 (18.9)36.2 (20.6)25.4 (13.9)<0.001
† KIDMED: Mediterranean Diet Quality Index for Children and Adolescents; KIDMED score (≤3: insufficient dietary habits, 4–7: relatively sufficient dietary habits, ≥8: sufficient dietary habits). * p-values for differences between boys and girls.
Table 2. Anthropometric and behavioral characteristics (mean ± SD) according to more than once daily or no vegetable consumption, in participants from both sexes.
Table 2. Anthropometric and behavioral characteristics (mean ± SD) according to more than once daily or no vegetable consumption, in participants from both sexes.
Vegetable Consumption
(>1 Daily)		No Vegetable
Consumption
BoysGirlsBoysGirls
Age (years) 11.1 (2.1)11.0 (2.1)11.4 (2.3) *11.4 (2.3) *
Body mass index (kg/m2)19.4 (3.8)19.2 (3.7)19.9 (3.9) *19.7 (3.8) *
Waist circumference (cm)71.0 (11.1)68.8 (10.1)71.9 (11.2)*69.8 (10.4)
WHtR0.32 (0.46)0.28 (0.45)0.33 (0.47) *0.29 (0.45) *
KIDMED score8.3 (2.2)8.2 (2.2)6.2 (2.3) *6.3 (2.1) *
Physical activity (h/wk) 11.2 (5.9)9.2 (5.4)10.0 (5.8) *8.2 (5.1)
Screen time (h/wk)8.5 (8.7)7.1 (7.8)9.5 (8.8) *8.2 (8.1) *
Sleep time (h/day)8.7 (1.5)8.8 (1.6)8.4 (1.4) *8.4 (1.5) *
20 m shuttle run (laps)36.4 (20.7)25.8 (12.9)35.2 (20.4) *24.0 (13.1) *
KIDMED: Mediterranean Diet Quality Index for Children and Adolescents; BMI: body mass index; WHtR: waist to height ratio; * p-values < 0.01 for differences between increased (>1/day) and non-consumers of vegetables, from the same sex.
Table 3. Results (OR, 95%CI) from logistic regression models used to evaluate the association of children’s dietary habits with increased vegetable consumption (no vs. yes).
Table 3. Results (OR, 95%CI) from logistic regression models used to evaluate the association of children’s dietary habits with increased vegetable consumption (no vs. yes).
PredictorsModel 1
OR (95% CI)		Model 2
OR (95% CI)		Model 3
OR (95% CI)
Boys
Skips breakfast (no vs. yes)1.04 (0.99–1.08)1.02 (0.98–1.06)1.02 (0.98–1.06)
Eats a second fruit every day (no vs. yes)1.85 (1.79–1.91)1.81 (1.75–1.87)1.78 (1.72–1.84)
Consumes fish regularly (at least 2–3/week) (no vs. yes)0.95 (0.92–0.98)0.98 (0.95–0.99)1.01 (0.98–1.03)
Eats pulses >1/week (no vs. yes)1.54 (1.49–1.61)1.57 (1.51–1.63)1.54 (1.48–1.60)
Eats pasta or rice almost every day (no vs. yes)1.29 (1.25–1.33)1.29 (1.25–1.33)1.28 (1.24–1.33)
Consumes nuts regularly (at least 2–3/week) (no vs. yes)1.36 (1.31–1.40)1.34 (1.30–1.38)1.32 (1.28–1.36)
Uses olive oil at home (no vs. yes)0.85 (0.78–0.93)0.89 (0.80–1.00)0.93 (0.85–1.03)
Takes two yoghurts and/or some cheese daily (no vs. yes)1.26 (1.21–1.31)1.27 (1.22–1.32)1.27 (1.21–1.32)
Fast food consumption (<=1/week vs. >1/week)1.02 (0.98–1.06)0.96 (0.88–1.04)0.95 (0.95–1.01)
Takes sweets/candy several times every day (no v. yes)0.92 (0.88–0.96)0.94 (0.90–0.98)0.95 (0.91–0.99)
Girls
Skips breakfast(no vs. yes)1.02 (0.98–1.06)1.00 (0.97–1.05)0.99 (0.92–1.05)
Eats a second fruit every day (no vs. yes)1.83 (1.77–1.89)1.80 (1.73–1.85)1.76 (1.70–1.82)
Consumes fish regularly (at least 2–3/week) (no vs. yes)0.94 (0.91–0.97)0.97 (0.94–1.00)0.99 (0.96–1.02)
Eats pulses >1/week (no vs. yes)1.45 (1.40–1.49)1.46 (1.40–1.51)1.44 (1.39–1.49)
Eats pasta or rice almost every day (no vs. yes)1.20 (1.16–1.24)1.20 (1.16–1.24)1.19 (1.15–1.23)
Consumes nuts regularly (at least 2–3/week) (no vs. yes)1.35 (1.31–1.40)1.34 (1.30–1.38)1.32 (1.28–1.36)
Uses olive oil at home (no vs. yes)0.89 (0.81–0.98)0.91 (0.82–1.01)0.92 (0.84–1.01)
Takes two yoghurts and/or some cheese (40 g/daily) (no vs. yes)1.25 (1.19–1.29)1.25 (1.20–1.30)1.24 (1.20–1.28)
Fast food consumption (<=1/week vs. >1/week)0.98 (0.94–1.04)1.00 (0.95–1.05)0.99 (0.94–1.04)
Takes sweets/candy several times every day (no v. yes)0.86 (0.82–0.90)0.89 (0.85–0.94)0.90 (0.86–0.94)
Model 1: unadjusted; Model 2: adjusted for age, BMI, and waist circumference; Model 3: Model 2 + screen time, sleeping hours, and physical activity hours.
Table 4. Results (OR, 95% CI) from logistic regression models used to evaluate the association of children’s (8 to 17 years old) characteristics with increased vegetable consumption (no vs. yes).
Table 4. Results (OR, 95% CI) from logistic regression models used to evaluate the association of children’s (8 to 17 years old) characteristics with increased vegetable consumption (no vs. yes).
PredictorsModel 1
OR (95% CI)		Model 2
OR (95% CI)		Model 3
OR (95% CI)		Model 4
OR (95% CI)
Boys
Age (per 1 year)0.92 (0.91–0.93)0.93 (0.91–0.94)0.93 (0.92–0.94)0.92 (0.90–0.92)
BMI category (normal weight vs. overweight/obese)0.80 (0.72–0.87)0.81 (0.74–0.88)0.80 (0.72–0.88)0.80 (0.72–0.87)
Abdominal obesity (no vs. yes)0.87 (0.84–0.92)0.89 (0.85–0.92)0.89 (0.86–0.93)0.90 (0.87–0.94)
KIDMED index (insufficient vs. relatively/sufficient
dietary habits) 		3.60 (3.49–3.70)3.59 (3.50–3.69)3.60 (3.51–3.68)
Sleeping hours (sufficient vs. insufficient) 0.80 (0.76–0.83)0.80 (0.77–0.84)
Screen time (acceptable vs. increased) 0.81 (0.77–0.85)0.80 (0.76–0.84)
Physical activity (adequate vs. inadequate) 0.70 (0.65–0.75)
20 m shuttle run (per 1 lap) 1.02 (1.00–1.03)
Girls
Age (per 1 year)0.90 (0.88–0.92)0.90 (0.89–0.91)0.90 (0.88–0.91)0.91 (0.89–0.93)
BMI group (normal weight vs. overweight/obese)0.81 (0.74–0.88)0.80 (0.73–0.86)0.81 (0.74–0.89)0.82 (0.74–0.89)
Central obesity (no vs. yes) 0.88 (0.85–0.92)0.89 (0.86–0.92)0.90 (0.88–0.93)0.91 (0.89–0.94)
KIDMED index (relatively/sufficient vs. insufficient
dietary habits		 3.70 (3.56–3.81)3.69 (3.60–3.78)3.68 (3.59–3.70)
Sleeping hours (sufficient vs. insufficient) 0.84 (0.81–0.87)0.83 (0.80–0.86)
Screen time (acceptable vs. increased) 0.83 (0.80–0.0.86)0.81 (0.78–0.84)
Physical activity (adequate vs. inadequate) 0.71 (0.66–0.77)
20 m shuttle run (per 1 lap) 1.01 (1.00–1.02)
Model 1: adjusted for age and BMI group and abdominal obesity; Model 2: Model 1 + KIDMED index; Model 3: Model 2 + screen score and sleeping level; Model 4: Model 3 + physical activity levels and aerobic fitness measurements.
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Tambalis, K.D.; Tampalis, D.; Panagiotakos, D.B.; Sidossis, L.S. Examining the Relationship Between Increased Vegetable Consumption and Lifestyle Characteristics Among School-Aged Children: A Descriptive Study. Appl. Sci. 2025, 15, 8665. https://doi.org/10.3390/app15158665

AMA Style

Tambalis KD, Tampalis D, Panagiotakos DB, Sidossis LS. Examining the Relationship Between Increased Vegetable Consumption and Lifestyle Characteristics Among School-Aged Children: A Descriptive Study. Applied Sciences. 2025; 15(15):8665. https://doi.org/10.3390/app15158665

Chicago/Turabian Style

Tambalis, Konstantinos D., Dimitris Tampalis, Demosthenes B. Panagiotakos, and Labros S. Sidossis. 2025. "Examining the Relationship Between Increased Vegetable Consumption and Lifestyle Characteristics Among School-Aged Children: A Descriptive Study" Applied Sciences 15, no. 15: 8665. https://doi.org/10.3390/app15158665

APA Style

Tambalis, K. D., Tampalis, D., Panagiotakos, D. B., & Sidossis, L. S. (2025). Examining the Relationship Between Increased Vegetable Consumption and Lifestyle Characteristics Among School-Aged Children: A Descriptive Study. Applied Sciences, 15(15), 8665. https://doi.org/10.3390/app15158665

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