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25 August 2026

12 Pages

Dietary Habits of Sports Volunteers and Their Associations with Sociodemographic Characteristics: A Cross-Sectional Study

,
and
1
Department of Sports Tourism, Faculty of Physical Culture Sciences, Poznan University of Physical Education, 61-871 Poznan, Poland
2
Department of Rehabilitation, Faculty of Health Sciences in Katowice, Medical University of Silesia in Katowice, 40-751 Katowice, Poland
3
Department of Public Health, Faculty of Public Health in Bytom, Medical University of Silesia in Katowice, 41-902 Bytom, Poland
*
Author to whom correspondence should be addressed.
Dietetics2026, 5(4), 51;https://doi.org/10.3390/dietetics5040051
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Abstract

Background: Dietary behaviors are important modifiable factors related to health and well-being. Sports volunteers represent a population involved in sport-related environments, yet limited evidence is available regarding their specific dietary behaviors and their associations with sociodemographic characteristics. Materials and Methods: A cross-sectional survey was conducted among 297 sports volunteers (184 women, 113 men) at international sporting events in Poznan, Poland. Twenty specific dietary behaviors, including meal frequency, consumption of fruits, vegetables, cereal products, meat, dairy products, fish, sweets, sugar-sweetened beverages, salty snacks, ready-made meals, legumes, water, and selected eating behaviors, were assessed using a 20-item dietary habits questionnaire developed in accordance with dietary recommendations of the National Institute of Public Health and the National Center for Nutrition Education in Poland. Gender, age, educational attainment, and BMI were recorded. Chi-square tests were used to examine bivariate associations between each dietary behavior and sociodemographic characteristics. Multivariable logistic regression models were subsequently applied to dietary behaviors showing statistically significant bivariate associations. Results: Of the 20 dietary behaviors assessed, only a small number varied significantly according to sociodemographic characteristics. Women reported higher adherence to daily fruit consumption (46.8% vs. 28.1%; p = 0.002) and daily consumption of at least three portions of vegetables (44.1% vs. 28.9%; p = 0.013) than men. Participants aged 18 years differed from older age groups in daily fruit consumption and limiting meat consumption to no more than once per day (p < 0.05). Educational attainment was associated with daily fruit consumption, with participants with primary education reporting higher adherence than those with higher education (48.8% vs. 25.3%; p = 0.011). BMI was associated with several dietary behaviors, including fruit consumption, avoiding snacking between meals, and portion control (p < 0.05). In multivariable analyses, female gender remained independently associated with fruit and vegetable consumption, while younger age was independently associated with daily fruit consumption and lower meat consumption. BMI remained independently associated with daily fruit consumption, whereas educational attainment was no longer significantly associated with dietary behaviors after adjustment. Conclusions: Most of the 20 dietary behaviors assessed did not differ significantly according to the examined sociodemographic characteristics. The observed associations were limited to selected behaviors and varied by gender, age, educational attainment, and BMI. Further research should examine additional factors, including physical activity, volunteering characteristics, and socioeconomic conditions, that may contribute to dietary behaviors among sports volunteers.

1. Introduction

Dietary habits constitute one of the most significant modifiable determinants of human health and play a fundamental role in the prevention of chronic non-communicable diseases (NCDs), including cardiovascular diseases, obesity, type 2 diabetes, and certain types of cancer [1,2,3,4]. According to global public health estimates, unhealthy dietary patterns remain among the leading contributors to disease burden worldwide, accounting for a substantial proportion of premature mortality and disability-adjusted life years [5,6]. In response to these challenges, improving population-level dietary behaviors has become a key priority for health policy, preventive medicine, and public health research [7,8].
Dietary behaviors are shaped by a complex interaction of individual, social, and environmental factors [9]. Although personal preferences and nutritional knowledge influence food choices, dietary patterns are also affected by sociodemographic characteristics, lifestyle factors, and the food environment [9,10]. Previous systematic reviews indicate that demographic characteristics, including sex, age, educational background, and body weight status, may contribute to differences in dietary habits, although the strength and direction of these associations vary across populations [11]. In particular, sex differences in dietary behaviors have been reported in several populations, with women tending to report some health-oriented dietary behaviors more frequently than men, although the magnitude and consistency of these differences vary across dietary components and populations [12,13,14]. Men have also been reported in some studies to consume certain energy-dense foods more frequently; however, such differences should not be interpreted as reflecting uniformly poorer dietary quality across all dietary behaviors [12,13,14]. Physical activity may also be associated with dietary behaviors, as individuals who engage in regular exercise or sports often demonstrate different dietary patterns compared with less active individuals; however, these associations may vary depending on the population and type of exercises [15,16,17]. Understanding the determinants of dietary behaviors is essential for developing targeted health promotion strategies, particularly among young adults, who experience significant lifestyle changes during the transition to independent living and higher education [18].
Within this broader framework, relatively limited attention has been paid to the dietary habits of individuals involved in voluntary activities related to sport. Sports volunteers represent a unique population that plays an essential role in the organization of sporting events and community-based physical activity initiatives [19,20]. Despite the growing importance of sports volunteering, empirical evidence regarding the dietary habits of this population remains scarce. Previous studies involving sports volunteers have focused primarily on volunteer motivation, event management, psychological well-being, and sustainability, whereas health-related behaviors, including nutrition, have received considerably less attention [21,22]. Although nutrition may be an important aspect of health-related behaviors among volunteers, existing research on dietary habits has often focused on specific contextual factors rather than comprehensively examining dietary behaviors and their sociodemographic determinants [23]. Furthermore, research on sports volunteering has primarily focused on organizational, motivational, and participation-related aspects, suggesting a need to broaden the research agenda towards other dimensions of volunteers’ health and lifestyle behaviors [24].
To the best of our knowledge, no quantitative cross-sectional study has comprehensively examined the dietary habits of sports volunteers or their associations with sociodemographic characteristics. Therefore, the primary aim of the present study was to assess dietary habits among sports volunteers. A secondary objective was to examine whether these behaviors differed according to selected sociodemographic characteristics, including gender, age, educational attainment, and body mass index (BMI).

2. Materials and Methods

2.1. Study Design and Participants

The present research employed a descriptive, quantitative, cross-sectional design and included a sample of 297 sports volunteers (184 women and 113 men). The study sample consisted of individuals who met the following eligibility criteria: they had been engaged in sports volunteering for more than one year and had participated as volunteers in at least one international sporting event organized in May or June 2022 in Poznan, the capital of the Greater Poland region in Poland. The events included the Enea European Team Speedway Championship, the World Rowing Cup, the ICF Canoe Sprint and Paracanoe World Cup, and 4Foulee. These criteria were established to ensure that participants had sufficient experience in sports volunteering and were familiar with the organizational context of sport events, allowing this study to focus on individuals with meaningful involvement in this specific volunteer population. Detailed sociodemographic characteristics of the participants are presented in Table 1.
Table 1. Socio-Demographic Characteristics.

2.2. Ethics

Submission of the completed questionnaire was considered as providing informed consent to participate in this study. Before responding, participants were informed about the purpose and nature of this research. Participation in the survey was fully anonymous. Respondents were not asked to provide any personal information that could enable their identification.
According to Polish regulations, anonymous diagnostic surveys that do not involve medical intervention or experimental procedures are not classified as medical experiments and therefore do not require approval from a Bioethics Committee. The present study was non-interventional and did not involve any medical intervention, experimental procedures, or assessment of health outcomes. In this case, the interpretation that formal ethical approval was not required was formally confirmed in writing by the Chair of the Bioethics Committee operating at the Poznan University of Medical Sciences.

2.3. Research Tool

Data were collected using a structured questionnaire as part of a broader research project investigating the motivation, well-being, and engagement of sports volunteers, within which dietary behaviors constituted one of the analyzed domains. For the purposes of the present study, two sections of the questionnaire were analyzed.
The first section collected basic sociodemographic information, including gender (male/female), age, height, body weight, and educational attainment. Height and body weight were self-reported, and body mass index (BMI) was subsequently calculated as weight (kg) divided by height squared (m2), according to the World Health Organization (WHO) classification [25].
The second section consisted of an author-developed 20-item dietary habits questionnaire, constructed in accordance with the dietary recommendations of the National Institute of Public Health and the National Center for Nutrition Education in Poland [26,27]. The questionnaire was designed to assess adherence to selected dietary recommendations rather than to measure a single latent construct.
The dietary recommendations were as follows: consuming 4–5 meals per day; including cereal products as a component of daily meals; consuming at least three portions of vegetables daily; consuming at least two portions of fruit daily; limiting the consumption of meat and meat products; consuming fish and fish products regularly (at least once per week); including milk and dairy products in the daily diet; including eggs and egg products in the diet; limiting the consumption of sweets; limiting the consumption of sugar-sweetened beverages; limiting the consumption of salty snacks; limiting the consumption of ready-made meals and fast food; including legumes regularly in the diet; maintaining adequate hydration through regular water intake; consuming breakfast regularly; avoiding snacking between meals; choosing fresh and high-quality food products; paying attention to food quality, including ingredients, origin, and expiry date; controlling food quantity and portion size; preparing and consuming meals mainly at home.
Each item was answered using a dichotomous (“Yes”/”No”) response format. Responses consistent with current dietary recommendations received one point, whereas responses inconsistent with recommendations received zero points.
The questionnaire had previously been developed and applied in earlier studies involving adult respondents [28,29]. During the development process, its content validity was evaluated by a panel of 10 experts (four nutritionists–dietitians, three psychologists, and three public health researchers), who were selected based on their academic qualifications and professional experience in nutrition and health promotion. Based on their comments and recommendations, several items were revised to improve wording and content clarity before the final version was established. A formal Content Validity Index (CVI) was not calculated; instead, expert review was used to establish face and content validity during questionnaire development. Test–retest agreement was assessed using Scott’s π coefficient, which was selected because the questionnaire consisted of dichotomous (“Yes”/”No”) items and the pilot assessment was designed to evaluate the agreement between repeated categorical responses. Test–retest agreement demonstrated very good agreement for the majority of questionnaire items (π ranging up to 0.93), whereas the remaining items demonstrated good agreement (lowest π = 0.72), indicating satisfactory temporal stability. Because the questionnaire evaluates several distinct dietary behaviors rather than a single underlying construct, the individual items were analyzed independently.

2.4. Procedure

The questionnaire was administered online using the Google Forms platform. Participants were invited to complete the survey either on-site during the sporting events or remotely. During the events, the researcher provided a tablet device, allowing volunteers to complete the questionnaire immediately. Additionally, the survey link was distributed electronically after the events, enabling those who had not completed the questionnaire on-site to do so at a later time, including from home.
To ensure the reliability of the collected data and reduce the risk of falsified responses, several protective measures were implemented. These included monitoring the time required to complete the questionnaire and the use of a CAPTCHA verification system designed to prevent automated submissions by bots or programs that automatically fill in online forms.

2.5. Statistical Analysis

All statistical analyses and graphical visualizations were performed using Statistica 13.0 software (TIBCO Software Inc., Palo Alto, CA, USA). Statistical analyses were conducted to examine differences in adherence to selected dietary recommendations among sports volunteers according to sociodemographic characteristics, including gender, age, educational attainment, and body mass index (BMI).
The dietary behaviors analyzed in this study were based on the 20-item dietary habits questionnaire. Each questionnaire item represented an individual dietary behavior and was coded dichotomously according to adherence to the corresponding dietary recommendation (1 = adherence/“Yes”, 0 = non-adherence/“No”). The analyses were performed using individual dietary behavior items rather than a total questionnaire score or predefined adherence categories.
Descriptive analyses were performed first, including frequencies and percentage distributions for all categorical variables. These analyses were used to characterize the study population and describe the prevalence of adherence to each assessed dietary behavior.
Associations between sociodemographic variables and individual dietary behaviors were assessed using the chi-square test of independence (χ2). Because multiple bivariate comparisons were performed, raw p-values obtained from chi-square tests were adjusted using the Benjamini–Hochberg false discovery rate (FDR) procedure. Unless otherwise indicated, all p-values reported in the Results are FDR-adjusted p-values.
Multivariable logistic regression analyses were conducted for dietary behaviors that demonstrated statistically significant differences in the bivariate analyses. Each dietary behavior was analyzed separately as a binary outcome variable. The dependent variable was coded as 1 for adherence to the dietary recommendation and 0 for non-adherence. All models included the same set of covariates entered simultaneously using the enter method: age (18 years, 19–25 years, and 26–35 years), gender (male and female, with male as the reference category), educational attainment (primary, secondary, vocational, and higher education, with higher education as the reference category), and BMI. BMI was calculated from self-reported height and weight and classified according to the WHO criteria as underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (≥30.0 kg/m2). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding p-values were reported. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test, and multicollinearity was assessed using variance inflation factors (VIFs).

3. Results

3.1. Associations Between Sociodemographic Variables and Dietary Behaviors

The highest proportion of “Yes” responses in the total sample was observed for always starting the day with breakfast (218; 73.4%) and consuming milk and dairy products at least once per day (213; 71.7%). In contrast, the highest proportion of “No” responses was observed for consuming legumes at least once per day (264; 88.9%) and limiting sweets to no more than once per week (225; 75.8%). The distribution of responses across all dietary habit questionnaire items is presented in Table 2.
Table 2. Distribution of Responses to Individual Dietary Habit Questionnaire Items in the Total Sample.
Of the 20 dietary behaviors analyzed, only two differed significantly by gender. Women reported significantly higher adherence to these behaviors than men. Daily consumption of at least three portions of vegetables was reported by 44.1% of women compared with 28.9% of men (χ2(1) = 6.23, p = 0.013). Similarly, daily fruit consumption was more frequent among women (46.8%) than men (28.1%) (χ2(1) = 9.57, p = 0.002). No other assessed dietary behaviors differed significantly between women and men.
In analyses according to age groups, significant differences were observed for daily fruit consumption and meat intake frequency. Daily fruit consumption was reported by 50.9% of participants aged 18 years, 36.8% of those aged 19–25 years, and 21.3% of those aged 26–35 years (χ2(2) = 13.16, p = 0.004). Consumption of meat no more than once per day also differed significantly between age groups, being reported by 40.4% of participants aged 18 years, 23.5% of those aged 19–25 years, and 31.9% of those aged 26–35 years (χ2(2) = 8.17, p = 0.043).
Educational attainment was significantly associated with only one of the 20 assessed dietary behaviors, namely daily fruit consumption. This behavior was reported by 48.8% of participants with primary education and 25.3% of those with higher education, with a significant difference across educational groups (χ2(3) = 11.21, p = 0.011). No statistically significant differences between education groups were observed for the other assessed dietary behaviors, including vegetable consumption.
Statistically significant differences were found for three dietary behaviors across BMI categories. Daily fruit consumption was reported by 52.6% of participants with underweight, 44.6% of those with normal weight, 27.6% of those with overweight, and 16.7% of those with obesity (χ2(3) = 10.52, p = 0.015). Significant differences were also observed for avoiding snacking between meals (χ2(3) = 8.47, p = 0.037) and controlling food quantity (χ2(3) = 9.21, p = 0.026). For both behaviors, adherence was lower among participants with overweight and obesity than among participants in the lower BMI categories.

3.2. Multivariable Analysis

Multivariable logistic regression analysis was conducted to identify independent predictors of dietary behaviors that were statistically significant in the bivariate analysis. After adjustment for potential confounders, female gender remained a significant predictor of daily fruit consumption (OR = 2.11; 95% CI: 1.27–3.49; p = 0.004) and consumption of at least three portions of vegetables per day (OR = 1.87; 95% CI: 1.08–3.25; p = 0.026).
Being 18 years of age was independently associated with higher odds of daily fruit consumption (OR = 2.03; 95% CI: 1.14–3.61; p = 0.016) and lower meat consumption (≤1 time per day) (OR = 1.78; 95% CI: 1.02–3.10; p = 0.042) compared to older age groups.
Educational attainment was not significantly associated with daily fruit consumption after adjustment for other covariates (p = 0.080).
Body mass index (BMI) was independently associated with daily fruit consumption. Underweight participants had higher odds of daily fruit consumption compared with participants with overweight or obesity (OR = 2.13; 95% CI: 1.15–4.00; p = 0.016).

4. Discussion

The present study examined 20 specific dietary behaviors among sports volunteers and explored their associations with selected sociodemographic characteristics. Overall, only a small number of the assessed behaviors differed significantly according to gender, age, educational attainment, or BMI. Women reported higher adherence to daily fruit and vegetable consumption than men, while age, educational attainment, and BMI were associated with selected individual dietary behaviors. These findings should therefore be interpreted as associations involving specific dietary behaviors rather than as evidence of broadly different dietary patterns according to sociodemographic characteristics.
An important consideration when interpreting the present findings is their relationship to dietary behaviors observed in the general Polish population. Findings from the WOBASZ (Multicentre National Population Health Examination Survey), one of the largest nationwide epidemiological programs conducted in Poland, indicate that adherence to dietary recommendations among adults remains suboptimal and that some dietary behaviors differ between women and men [30]. The gender-related differences observed in the present study are therefore broadly consistent with patterns reported in the general population. However, because the present study was conducted in a convenience sample of sports volunteers and did not include a non-volunteer comparison group, it cannot be determined whether sports volunteers differ from the general Polish population with respect to dietary behaviors. Furthermore, differences in sampling strategies and dietary assessment methods limit direct comparisons between studies.
In the present study, gender was associated with only two of the 20 assessed dietary behaviors: daily fruit consumption and daily consumption of at least three portions of vegetables. Women reported higher adherence to both recommendations than men. These findings are consistent with previous research indicating that sex differences may occur for particular dietary behaviors, although the magnitude and direction of such differences vary across populations and dietary components [11,31]. Explanations proposed in previous research include differences in health awareness, nutritional knowledge, and motivation to follow dietary recommendations. However, the present findings should not be interpreted as indicating systematically healthier dietary behaviors among women, as no significant gender differences were observed across most of the 20 behaviors assessed.
Age was associated with two specific dietary behaviors: daily fruit consumption and frequency of meat intake. Daily fruit consumption was most frequently reported among participants aged 18 years and least frequently among those aged 26–35 years. Previous studies have identified early adulthood as an important period for the development and modification of dietary behaviors, as individuals may experience substantial changes in eating habits during the transition toward greater independence [27,28]. Changes in educational or occupational demands, daily routines, and living circumstances may contribute to differences in individual dietary behaviors during this period [32,33]. Nevertheless, the limited number of age-related associations observed in the present study does not support the conclusion that dietary behaviors broadly differed according to age.
Educational attainment was associated with only one of the 20 assessed dietary behaviors, namely daily fruit consumption. Participants with primary education reported this behavior more frequently than participants with higher education. This finding should be interpreted cautiously because the educational categories differed in size and may have reflected differences in age distribution or other unmeasured characteristics. Participants with primary education were predominantly younger, which may partly explain the observed association. In addition, socioeconomic circumstances, household characteristics, and access to food-related information may influence dietary behaviors independently of formal educational attainment [10,34,35]. The lack of significant associations for most of the assessed behaviors further suggests that educational attainment alone does not provide a comprehensive explanation of dietary behaviors in this sample.
BMI was associated with several individual dietary behaviors, including daily fruit consumption, avoiding snacking between meals, and controlling food quantity. Differences were observed across BMI categories, with varying levels of adherence to these specific recommendations. These findings are consistent with previous research indicating that dietary behaviors and weight status may be interrelated [36,37]. However, the cross-sectional design prevents determination of the direction of these associations. Dietary behaviors may contribute to differences in BMI, but dietary behaviors may also be influenced by weight status, previous attempts to modify body weight, physical activity, socioeconomic circumstances, psychological factors, or other unmeasured characteristics. Therefore, the observed associations should not be interpreted as evidence that particular dietary behaviors caused differences in BMI.
Taken together, the findings indicate that sociodemographic characteristics were associated with only a small number of the 20 dietary behaviors assessed. This limited pattern of associations is important when considering potential implications for health promotion. Rather than suggesting a need for broad conclusions about the dietary quality of sports volunteers, the findings may help identify specific behaviors that could be considered in future nutrition education initiatives. However, the present study did not assess whether nutrition education or other interventions would modify these behaviors. Future research should therefore use more comprehensive dietary assessment methods and investigate whether volunteering-related factors, including food availability during events, the duration and intensity of volunteering shifts, and the specific volunteer role, are associated with dietary behaviors. Longitudinal studies and studies including appropriate comparison groups are also needed to determine whether participation in sports volunteering is associated with changes in dietary behaviors over time.

5. Strengths and Limitations

A strength of the present study is that it addresses an underexplored aspect of sports volunteering by examining specific dietary behaviors in a population that has received limited attention in previous research. Furthermore, multivariable logistic regression analyses were performed to account for several measured sociodemographic characteristics simultaneously. Although this analytical approach cannot eliminate residual confounding, it provides adjusted estimates of the observed associations and allows for a more comprehensive interpretation of the relationships between individual dietary behaviors and selected sociodemographic characteristics.
However, the findings should be interpreted considering several limitations. First, this study was conducted among sports volunteers recruited during international sporting events organized in a single city in Poland. Therefore, the sample represents a convenience sample and should not be considered nationally or internationally representative of sports volunteers. The characteristics of sports volunteers may vary according to geographical region, type of sporting event, organizational context, and cultural factors.
Second, because this study did not include a comparison group from the general Polish population, it is not possible to determine whether the observed dietary behaviors are specific to sports volunteers or reflect patterns commonly observed among Polish adults. Although the results appear broadly consistent with findings from national population-based studies, including the WOBASZ program [30], differences in sampling procedures and dietary assessment methods limit direct comparisons.
Third, the cross-sectional design prevents conclusions regarding causal relationships between sociodemographic characteristics, dietary behaviors, and BMI. The observed associations should therefore be interpreted as correlational rather than causal.
Fourth, the assessment was limited to 20 specific dietary behaviors and therefore did not constitute a comprehensive assessment of overall dietary intake or diet quality. A more detailed assessment using validated dietary assessment methods, such as dietary recalls, food records, or validated food frequency questionnaires, could provide a more complete characterization of dietary behaviors in this population. Moreover, the use of self-reported, closed-ended questions may have introduced recall bias and social desirability bias, particularly for behaviors that correspond to commonly recognized dietary recommendations. Although the questionnaire underwent content validation, pilot testing, and reliability assessment, its psychometric properties have been evaluated only to a limited extent in sports volunteer populations. It also remains unclear whether participants reported their usual dietary behaviors or behaviors specifically related to the period of sports volunteering.
Fifth, socioeconomic characteristics were assessed only partially. Although educational attainment was included, other important socioeconomic determinants of dietary behaviors, including household income, employment status, and place of residence, were not collected. In addition, data collection was conducted exclusively through an online questionnaire. Although efforts were made to facilitate participation both during sporting events and remotely, responses completed outside the event environment could not be fully verified. Differences in internet access, digital literacy, or familiarity with online surveys may have influenced participation and introduced selection bias.
Finally, several potentially relevant lifestyle and contextual variables were not assessed, including physical activity level, the specific volunteer role performed during sporting events, the duration and intensity of volunteering shifts, and access to food and beverages during volunteering activities. These factors may influence individual dietary behaviors and should be incorporated into future research.

6. Conclusions

This study provides descriptive evidence of selected dietary behaviors among sports volunteers participating in international sporting events in Poland. Of the 20 dietary behaviors assessed, only a small number differed according to gender, age, educational attainment, or BMI. Although women, younger participants, and participants with lower BMI showed higher adherence to some individual dietary recommendations, these differences were limited to specific behaviors and should not be interpreted as evidence of broadly different dietary behaviors according to sociodemographic characteristics.
The findings describe dietary behaviors within a sample of sports volunteers and do not indicate that sports volunteering or participation in a sporting environment influences dietary behaviors. Future studies should use more comprehensive and validated methods to assess dietary behaviors and should consider potential social desirability bias associated with self-reported, closed-ended responses. Further research incorporating detailed dietary assessment, physical activity, volunteering characteristics, socioeconomic factors, and appropriate comparison groups may provide a more complete understanding of the factors associated with dietary behaviors among sports volunteers.

Author Contributions

Conceptualization, M.R. and M.G.; methodology, M.R. and M.G.; software, M.R., T.J. and M.G.; validation, M.R. and M.G.; formal analysis, M.R. and M.G.; investigation, M.R.; resources, M.R.; data curation, M.R. and M.G.; writing—original draft preparation, M.R.; writing—review and editing, M.R., T.J. and M.G.; visualization, M.R.; supervision, M.R.; project administration, M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. According to Polish regulations, anonymous diagnostic surveys that do not involve medical intervention or experimental procedures are not classified as medical experiments and therefore do not require approval from a Bioethics Committee. The present study was non-interventional and did not involve any medical intervention or experimental procedures. This interpretation was formally confirmed in writing by the Chair of the Bioethics Committee at the Poznan University of Medical Sciences.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the lack of access to the public repository.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Afshin, A.; Sur, P.J.; Fay, K.A.; Cornaby, L.; Ferrara, G.; Salama, J.S.; Mullany, E.C.; Abate, K.H.; Abbafati, C.; Abebe, Z.; et al. Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019, 393, 1958–1972. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  2. Qiao, J.; Lin, X.; Wu, Y.; Huang, X.; Pan, X.; Xu, J.; Wu, J.; Ren, Y.; Shan, P.F. Global burden of non-communicable diseases attributable to dietary risks in 1990–2019. J. Hum. Nutr. Diet. 2022, 35, 202–213. [Google Scholar] [CrossRef] [Scilit]
  3. Meier, T.; Gräfe, K.; Senn, F.; Sur, P.; Stangl, G.I.; Dawczynski, C.; März, W.; Kleber, M.E.; Lorkowski, S. Cardiovascular mortality attributable to dietary risk factors in 51 countries in the WHO European Region from 1990 to 2016: A systematic analysis of the Global Burden of Disease Study. Eur. J. Epidemiol. 2019, 34, 37–55. [Google Scholar] [CrossRef] [Scilit]
  4. Pörschmann, T.; Meier, T.; Lorkowski, S. Cardiovascular mortality attributable to dietary risk factors in 54 countries in the WHO European Region from 1990 to 2019: An updated systematic analysis of the Global Burden of Disease Study. Eur. J. Prev. Cardiol. 2025, 32, 1553–1563. [Google Scholar] [CrossRef] [Scilit]
  5. Micha, R.; Peñalvo, J.L.; Cudhea, F.; Imamura, F.; Rehm, C.D.; Mozaffarian, D. Association Between Dietary Factors and Mortality From Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA 2017, 317, 912–924. [Google Scholar] [CrossRef] [Scilit]
  6. Bamia, C.; Trichopoulos, D.; Ferrari, P.; Overvad, K.; Bjerregaard, L.; Tjønneland, A.; Halkjær, J.; Clavel-Chapelon, F.; Kesse, E.; Boutron-Ruault, M.-C.; et al. Dietary patterns and survival of older Europeans: The EPIC-elderly study (European Prospective Investigation into Cancer and Nutrition). Public Health Nutr. 2007, 10, 590–598. [Google Scholar] [CrossRef] [Scilit]
  7. Swinburn, B.A.; Kraak, V.I.; Allender, S.; Atkins, V.J.; Baker, P.I.; Bogard, J.R.; Brinsden, H.; Calvillo, A.; De Schutter, O.; Devarajan, R.; et al. The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report. Lancet 2019, 393, 791–846. [Google Scholar] [CrossRef] [Scilit]
  8. Mozaffarian, D.; Angell, S.Y.; Lang, T.; Rivera, J.A. Role of government policy in nutrition—Barriers to and opportunities for healthier eating. BMJ 2018, 361, k2426. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  9. Story, M.; Kaphingst, K.M.; Robinson-O’Brien, R.; Glanz, K. Creating healthy food and eating environments: Policy and environmental approaches. Annu. Rev. Public Health 2008, 29, 253–272. [Google Scholar] [CrossRef] [Scilit]
  10. Darmon, N.; Drewnowski, A. Does social class predict diet quality? Am. J. Clin. Nutr. 2008, 87, 1107–1117. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  11. Sleddens, E.F.; Kroeze, W.; Kohl, L.F.; Bolten, L.M.; Velema, E.; Kaspers, P.; Kremers, S.P.; Brug, J. Correlates of Dietary Behavior in Adults: An Umbrella Review. Nutr. Rev. 2015, 73, 477–499. [Google Scholar] [CrossRef] [Scilit]
  12. Wardle, J.; Haase, A.M.; Steptoe, A.; Nillapun, M.; Jonwutiwes, K.; Bellisie, F. Gender Differences in Food Choice: The Contribution of Health Beliefs and Dieting. Ann. Behav. Med. 2004, 27, 107–116. [Google Scholar] [CrossRef] [Scilit]
  13. Fagerli, R.A.; Wandel, M. Gender Differences in Opinions and Practices with Regard to a “Healthy Diet”. Appetite 1999, 32, 171–190. [Google Scholar] [CrossRef] [Scilit]
  14. McKenzie, B.L.; Coyle, D.H.; Burrows, T.; Rosewarne, E.; Peters, S.A.; Carcel, C.; Collins, C.E.; Norton, R.; Woodward, M.; Jaacks, L.M.; et al. Gender differences in the accuracy of dietary assessment methods to measure energy intake in adults: Protocol for a systematic review and meta-analysis. BMJ Open 2020, 10, e035611. [Google Scholar] [CrossRef] [Scilit]
  15. Papale, O.; Festino, E.; Di Rocco, F.; De Maio, M.; Foster, C.; Cortis, C.; Fusco, A. Eating Right, Sleeping Tight? A Cross-Sectional Study on the Student-Athlete Paradox for Diet and Sleep Behaviors. Nutrients 2025, 17, 2946. [Google Scholar] [CrossRef] [Scilit]
  16. Calella, P.; Gallè, F.; Di Onofrio, V.; Cerullo, G.; Liguori, G.; Valerio, G. Adherence to Mediterranean diet in athletes: A narrative review. Sport Sci. Health 2022, 18, 1141–1148. [Google Scholar] [CrossRef] [Scilit]
  17. Sajdakowska, M.; Gutkowska, K.; Kosicka-Gębska, M.; Gębski, J.; Gantner, A. Association between Physical Activity, Diet Quality and Leisure Activities of Young Poles. Nutrients 2023, 15, 5121. [Google Scholar] [CrossRef] [Scilit]
  18. Mikkilä, V.; Räsänen, L.; Raitakari, O.T.; Pietinen, P.; Viikari, J. Consistent dietary patterns identified from childhood to adulthood: The Cardiovascular Risk in Young Finns Study. Br. J. Nutr. 2005, 93, 923–931. [Google Scholar] [CrossRef] [Scilit]
  19. Doherty, A. The volunteer legacy of a major sport event. J. Policy Res. Tour. Leis. Events 2009, 1, 185–207. [Google Scholar] [CrossRef] [Scilit]
  20. Fithroni, H.; Nurhasan, N.; Setijono, H.; Aman, M.S.; Dafun, P.B., Jr.; Kartiko, D.C.; Ilham, I.; Mario, D.T.; Komaini, A.; Ayubi, N. Sports Volunteer Involvement in Strategies to Increase Sports Community Participation: Systematic Review. Retos 2024, 51, 373–376. [Google Scholar] [CrossRef] [Scilit]
  21. Rozmiarek, M. The Role of Nutrition in Maintaining the Health and Physical Condition of Sports Volunteers. Nutrients 2024, 16, 3336. [Google Scholar] [CrossRef] [Scilit]
  22. Rozmiarek, M. Nutritional Education Needs and Preferences of Sports Volunteers: Access, Expectations, and Forms of Support. Nutrients 2024, 16, 3568. [Google Scholar] [CrossRef] [Scilit]
  23. Souza, R.V.; Sarmento, R.A.; de Almeida, J.C.; Canuto, R. The effect of shift work on eating habits: A systematic review. Scand. J. Work Environ. Health 2019, 45, 7–21. [Google Scholar] [CrossRef] [Scilit]
  24. Rozmiarek, M. Research Trends in Sports Volunteering: A Focus on Polish Contributions to Global Knowledge. Sustainability 2024, 16, 8412. [Google Scholar] [CrossRef] [Scilit]
  25. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation; WHO Technical Report Series No. 894; WHO: Geneva, Switzerland, 2000. [Google Scholar]
  26. Jarosz, M.; Rychlik, E.; Stoś, K. Normy Żywienia Dla Populacji Polskiej i ich Zastosowanie; NIZP-PZH: Warszawa, Poland, 2020; pp. 103–110.
  27. Narodowe Centrum Edukacji Żywieniowej. Talerz Zdrowego Żywienia. 2020. Available online: https://ncez.pzh.gov.pl/abc-zywienia/talerz-zdrowego-zywienia/ (accessed on 30 March 2026).
  28. Grajek, M.; Krupa-Kotara, K.; Sas-Nowosielski, K.; Misterska, E.; Kobza, J. Prevalence of Orthorexia in Groups of Students with Varied Diets and Physical Activity (Silesia, Poland). Nutrients 2022, 14, 2816. [Google Scholar] [CrossRef] [Scilit]
  29. Rozmiarek, M.; Grajek, M.; Krupa-Kotara, K.; Malchrowicz-Mośko, E. Orthorectic behavior among students and motivation for physical activity, dietary habits, and restrictive eating. Front. Nutr. 2024, 11, 1367767. [Google Scholar] [CrossRef] [Scilit]
  30. Kwaśniewska, M.; Bielecki, W.; Kaczmarczyk-Chałas, K.; Pikala, M.; Drygas, W. Ocena rozpowszechnienia zdrowego stylu życia wśród dorosłych mieszkańców województwa łódzkiego i lubelskiego—Projekt WOBASZ. Prz. Lek. 2007, 64, 61–64. [Google Scholar]
  31. Leblanc, V.; Bégin, C.; Corneau, L.; Dodin, S.; Lemieux, S. Gender differences in dietary intakes: What is the contribution of motivational variables? J. Hum. Nutr. Diet. 2015, 28, 37–46. [Google Scholar] [CrossRef] [Scilit]
  32. Winpenny, E.M.; Smith, M.; Penney, T.; Foubister, C.; Guagliano, J.M.; Love, R.; Clifford Astbury, C.; van Sluijs, E.M.F.; Corder, K. Changes in physical activity, diet, and body weight across the education and employment transitions of early adulthood: A systematic review and meta-analysis. Obes. Rev. 2020, 21, e12962. [Google Scholar] [CrossRef] [Scilit]
  33. Munt, A.E.; Partridge, S.R.; Allman-Farinelli, M. The barriers and enablers of healthy eating among young adults: A missing piece of the obesity puzzle: A scoping review. Obes. Rev. 2017, 18, 171–183. [Google Scholar] [CrossRef] [Scilit]
  34. Thorpe, M.G.; Milte, C.M.; Crawford, D.; McNaughton, S.A. Education and lifestyle predict change in dietary patterns and diet quality of adults 55 years and over. Nutr. J. 2019, 18, 67. [Google Scholar] [CrossRef] [Scilit]
  35. Olstad, D.L.; McIntyre, L. Educational attainment as a super determinant of diet quality and dietary inequities. Adv. Nutr. 2025, 16, 100482. [Google Scholar] [CrossRef] [Scilit]
  36. Farsi, D.J. Diet Quality and Physical Activity and Their Association with BMI and Dental Caries Among High School Adolescents: A Cross-Sectional Study. Children 2024, 11, 1282. [Google Scholar] [CrossRef] [Scilit]
  37. Ford, D.W.; Hartman, T.J.; Still, C.; Wood, C.; Mitchell, D.; Hsiao, P.Y.; Bailey, R.; Smiciklas-Wright, H.; Coffman, D.L.; Jensen, G.L. Diet-related practices and BMI are associated with diet quality in older adults. Public Health Nutr. 2014, 17, 1565–1569. [Google Scholar] [CrossRef] [Scilit]
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