Obesity, food habits and socio-demographic factors among university students in Germany: a cross-sectional study

Obesity, food habits and socio-demographic factors among university students in Germany: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Obesity, food habits and socio-demographic factors among university students in Germany: a cross-sectional study Dominic Lemken, Ana Estevez Magnasco, Monika Hartmann, Leonie Bach, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6722133/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Obesity represents a growing public health challenge globally, with rising prevalence among university students. Its causes extend beyond diet and exercise, encompassing socioeconomic background, experiences of discrimination, and family education and migration history. This study examined obesity among German university students using data from the 2016 Studitemps survey, which included over 9,960 participants and mirrors key characteristics of national statistics and the largest social study of German students “Sozialerhebung 2016”. Findings reveal that obesity prevalence was higher among male students and increased with age, ranging from 3.4% in the youngest group to 9.8% in those aged 30–34. Students with less-educated parents or with a migration background showed notably higher obesity rates. Additionally, those living alone, relying on student loans, or frequently using food delivery services were more likely to be obese, while those living in shared flats had lower rates. Public and university initiatives should focus on at-risk groups by improving access to shared accommodations, promoting healthier food choices to reduce reliance on delivery services, and addressing cultural challenges faced by students from migrant or lower-education households. In conclusion, this study highlights the complex interplay of socio-demographic and lifestyle factors in obesity among university students. Health sciences/Risk factors Biological sciences/Physiology/Ageing Obesity Overweight University students Socio-cultural factors Risk factors Population health Living conditions Dietary Patterns Financial dependency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Non-communicable diseases (NCDs), including cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes, are the leading cause of death worldwide, accounting for 71% of all deaths globally 1 , 2 . Key risk factors directly linked to the development of NCDs include overweight and obesity. Moreover, several risk factors contribute to the development of overweight and obesity, including unhealthy diets, low physical activity, and sedentary lifestyle behavior 1 , 3 – 5 . As a key protagonist of chronic disease, obesity is a significant public health concern globally, with its prevalence steadily increasing in most demographics in most countries for which data are available. This steady increase also affects university students. University life is recognized as a critical phase where lifestyle habits formed can substantially influence long-term health outcomes. Nevertheless, there is a gap in research highlighting university student´s specific sociodemographic determinants of obesity. The rising incidence of obesity even among university students is concerning, due to the associated risk of chronic diseases such as diabetes, cardiovascular disease, and certain cancers. More specifically, several diseases related to obesity and overweight normally found in later stages of life, show a growing incidence among younger population groups. Such is the case with diet-related-NCD such as heart disease and early onset of type 2 diabetes 6 . As young adults, students consolidate habits they acquired during their childhood and adolescence and develop new ones that will be present during the rest of their lives, as it has been shown for food, alcohol and nicotine consumption 1 , 7 , 8 . Additionally, obesity can impair not only physical health but also academic performance and overall quality of life, leading to long-term socioeconomic consequences. The transition to university life often involves changes in dietary habits regarding food preparation, physical activity levels, and stress management, all of which are crucial in impacting the risk of overweight and obesity 8 , 9 . University students are susceptible, for example, to limited disposable income, restricted cooking skills, and insufficient nutrition knowledge, leading to poor food choices 8 . Moreover, this period can exacerbate and expose students to pre-existing or new ranges of stressors due to their transition process from adolescence to adulthood, such as academic pressure, changes in peer and family social supports and the exposure to participating in risky behaviours such as alcohol and drug use 8 , 9 . The implications of obesity extend beyond immediate health concerns, potentially leading to chronic diseases that affect long-term health and economic productivity. The need for integrated socio-political as well as scientific approaches to improve dietary patterns at the population level is clear 10 . Health equity is a critical lens through which the issue of obesity should increasingly be examined. This also applies to Germany. Even before student life, socio-economic disparities already exist in children, where obesity rates are 9.9% among lower and 2.3% among higher socio-economic status groups based on the KIGGS panel 11 . In 11 years the prevalence of overweight among children from lower socioeconomic households increased significantly (from 20–25.5%), while it decreased among children from higher socioeconomic status households (from 10.2 to 7.7%) 11 . These disparities continue into adolescence. For young adults, stress plays a crucial role in the development and progression of obesity. In Germany, chronic stress is particularly associated with lower socio-economic status 12 . Factors such as access to nutritious food, opportunities for physical activity, and health education can be limited for all population groups, including university students, potentially exacerbating the risk of obesity 13 – 15 . Additionally, cultural and familial influences, particularly among university students with a migration background, have been linked to obesity in some countries 16 . In Germany, official statistics on university students and obesity are lacking, so a representative assessment of obesity-related socioeconomic factors and dietary behaviors is still missing. By analyzing these variables, this study takes a multifaceted approach that acknowledges the heterogeneity among university students. It aims to contribute to the development of targeted public health interventions and policies that promote health equity while effectively addressing obesity within this population. Given the complex interplay of socio-demographic and lifestyle factors, this study aims to address the following research questions in the German context: Are socio-demographic factors (age, gender, education of parents, migration status and income) associated with obesity among university students? Are lifestyle and dietary factors (living situation, relationship status, food preparation modes, food category consumption frequencies) associated with obesity among university students? RESULTS Age and gender Among students, obesity rates rise from 3.4% in the 18-19 age group to 9.8% in the 30 to 34 age group (Figure 1). While both women and men experience similar trends in growth of obesity prevalence with age, women consistently exhibit lower rates across all age groups. Of note, differences between population and student obesity increase from 25 years onwards. Parents: Education and Migration Two key formal educational qualifications are recognized in the following: firstly, the “(Fach)Abitur,” a secondary school diploma necessary for university admission in Germany (UEL); and secondly, university degrees (UD) such as Bachelor or Diploma degrees, although the former is uncommon among the parent generation in Germany. The prevalence of UEL and UD among students’ parents can be accessed in the Appendix (Table A2) We see a disparity in the educational backgrounds of parents of students with obesity compared to their peers (Fig. 2 ). The differences are particularly pronounced for the most and least educated groups. A university degree of both parents is linked to an obesity rate of 4% [3.1–4.9] among students, which is significantly lower than students with parents with none of these formal education achievements, where the rates are at 7.8% [6.8–8.5]. The migration background of student families is diverse, commonly including origins such as Turkey, Russia, Poland, and Vietnam. A comparison between students with a migration background and those without shows a significant difference in obesity rates, at 5% [4.7–5.8] and 8% [6.7-9.0] respectively (Fig. 3 ). There are large differences between migration origins, e.g. 12% for Turkish immigrants and 5% for Polish ones, but limited subgroup size (372 and 137 respectively) does not allow for reliable estimates. Student Income In Germany, students rely on four primary sources of income: a) the subsidized public student loan system (Bafög), b) private sector student loans, c) income from private individuals such as parents, relatives, friends, or partners, and d) the student job market, which encompasses all types of formalized part-time employment. Overall, there is no significant difference in total income (Kwallis p-value=0.5689) between students with obesity, who average 953€ [878-1029], and students without obesity, who average 958€ [939-977]. However, significant differences exist in the sources of income, with students with obesity receiving more from loans (216 [163-268]€ vs. 138€ [129-146€]) and students without obesity obtaining more from private individuals (354€ [305-329] vs. 291€ [194-281]) (Figure 4). Income from student jobs is similar, although students with obesity exhibit a 5 percentage points lower employment rate (Kwallis p-value= 0.0235). Relationship status and living situation of students The primary forms of student living arrangements include residing: a. with parents or relatives, b. alone or with a partner, typically a joint household arrangement, c. in student-specific housing, d. in a shared flat, typically consisting of joint and individual household structures, e.g. through sub-renting. Sub-renting is the least common, with only 4% of students currently engaged in such arrangements. This scarcity leads to great uncertainty in estimating obesity rates for this group. Obesity rates are lowest among students living in shared flats, at 3.9% [3.1–4.5], whereas the highest rates, ranging from 7.1% [6.0-8.1] to 7.5% [6.5–8.5], occur among students living with parents, relatives, alone, or with a partner (Fig. 5 ). Additionally, significant differences in obesity rates correlate with relationship status; unmarried students in relationships exhibit the lowest rates with 5.2% [4.5–5.8], while married students show the highest 8.3% [6.5–10.1] (Fig. 5 ). Married students are also the oldest group with an average age of 24.4 years compared to 22.9 and 23.4 for no relationship and unmarried relationship. 2.3% of all students have children; 12% [7.8–16.4] of students with children have obesity, but the small number of cases precludes a reliable estimate. Food habits of students We examine how often students engage in cooking for themselves, dining at canteens and restaurants, consuming meals prepared by parents or relatives, consuming ready-made meals, and ordering from food delivery services. Inquiring about the frequency with which students prepare or obtain food through these channels—ranging from never to once per week or less, multiple times a week, to daily—reveals that students with and without obesity have on average similar habits, with the exception of food delivery services. Specifically, a Kruskal-Wallis test indicates no significant differences in the use of ready-made meals (p-value: 0.1427) or any other food acquisition methods. However, students with obesity significantly order more often from delivery services (p-value=0.0001) with a frequency score of 0.74[0.70-0.79] vs. 0.62 [0-61-0.63] (Figure 6). Similarly, the frequency of consumption across various food categories is largely comparable between students with and without obesity. The sole significant difference is in meat consumption, where there is a large variance, with students with obesity averaging 1.9 [1.79–1.93] compared to 1.6 [1.58–1.62] for students without obesity on a 4-point Likert scale (p-value: 0.0001) (Fig. 7 ). Consumer interest in food product attributes differs significantly between attributes commonly labelled, namely organic, fair-trade, regional and vegetarian products. When asked whether consumer consider these attributes during food purchases, students with obesity are less likely to consider them. The implications of such consumer behavior are not clear at this point (Figure 8). Probabilities in a multivariate analysis We conducted a multivariate analysis that incorporates various factors associated with obesity into a single regression model to account for interdependencies. For instance, this model assesses whether being married remains a risk factor when controlling for age, gender and other factors. It includes a range of previously introduced variables such as socio-demographic characteristics, living situations, relationship status, and food preparation modes. A confidence interval that does not include 1 indicates a significant effect at an alpha 5% level, meaning the relative chance of obesity changes with the associated factor. For example, odds ratios (OR) of 2 suggest a doubled chance of having obesity, as seen in individuals living with parents or relatives compared to those living in shared flats, while an OR of 0.5 indicates a halved chance. The independent variables are non-standardized, which means that marginal effect sizes, expressed as odds ratios (ORs), are not directly comparable but are more straightforward to interpret. For instance, each additional year of student age is associated, on average, with a 1.078 times higher likelihood of obesity, while each additional formal education degree attained by parents is associated with a 0.876 times lower likelihood of obesity. Multiplying effects, the results show that a student living with parents (OR = 2.071), ordering frequently from delivery services (OR = 1.502), and without a claimed relationship (OR = 1.347) has a fourfold increased probability of having obesity (cumulative OR = 4.2). DISCUSSION Main Findings : This study aimed to investigate the multifaceted nature of obesity among university students in Germany, with a particular focus on socioeconomic, sociodemographic, lifestyle and dietary factors. The findings provide critical insights into the underlying factors associated with obesity in this demographic and the factors not associated. Such factors must not be causally linked to obesity but can be used to identify groups at higher risk for obesity, which may help to inform health promotion efforts. Obesity is less prevalent among university students in Germany compared to the general population, especially after the age 25 is reached and with a slightly higher occurrence among men. The gender difference in obesity prevalence is smaller in the student population than in the general population (Fig. 1 ). Although obesity is less prevalent among students compared to the general population, the increase in students’ obesity rates with age seen in our study mirrors that of the broader population (Fig. 1 ). Students whose parents both hold university degrees have a lower prevalence of obesity compared to those whose parents did not pass a university entry examination (Fig. 2 ). Additionally, students with a migration background are more likely to experience obesity (Fig. 3 ). Monthly income does not significantly differ between students with and without obesity; however, students with obesity are more dependent on loans (Fig. 4 ). Students with obesity are also less likely to live in shared flats (Fig. 5 ), order from food delivery services more frequently (Fig. 6 ), and place less emphasis on labelled food attributes (Fig. 8). Furthermore, they consume meat more often than students without obesity (Fig. 7 ). The here mentioned factors remain significantly correlated with obesity even when included together in a joint model (Table 2 ). Strength and Limitations Our study possesses several relevant strengths. To our knowledge, it is the only study to provide detailed data on obesity prevalence and relevant determinants specifically among university students in Germany. The sample is large and representative of the German university student population concerning state of residence, employment status, income, type of institution, and term of enrollment, enhancing generalizability. Data collection was supported by multiple quality assurance measures, contributing to the robustness of findings. This study also has several limitations. First, it relies on self-reported data for weight and height, which may introduce reporting biases. Second, we use BMI as the primary criterion for defining overweight and obesity, despite recognized limitations in its ability to reflect overall health accurately. Finally, the study’s cross-sectional design restricts our capacity to infer causality between the identified determinants and obesity, limiting conclusions to associations rather than cause-and-effect relationships. Interpretation The findings from this study reveal nuanced associations between socioeconomic and sociodemographic factors, parental background, living arrangements, and obesity risk among university students in Germany. Unlike in previous research 17 , we did not find a positive relationship between lower household income and obesity risk; however, in this study, this risk manifests specifically among students reliant on loans. This loan dependency could contribute to elevated stress levels due to financial pressures, as students face the dual burden of repaying loans while aiming to complete their studies within a limited timeframe. Reliance on parental support may provide financial stability without the associated stress. Heightened stress from financial constraints may, therefore, exacerbate obesity risk, aligning with findings that stress can impact eating habits and weight gain 18 . Parental education level also appears to play a key role, emphasizing the impact of early-life socio-economic conditions on obesity risk, even in a relatively young and educated demographic. This finding highlights the influence of parental education as a lasting factor that may shape health behaviors and lifestyle choices into adulthood. Similarly, the migration background of students is linked with higher obesity rates, supporting evidence from previous research that students with migrant parents are at increased risk 17 . This association could reflect various influences, including cultural dietary patterns, socioeconomic challenges that migrant families may face, and possibly limited access to health-promoting resources in neighborhoods with higher migrant populations. Other factors can be a lack of family resources in terms of knowledge and to provide healthy foods at home 19 . Food accessibility of culturally appropriate ultra-processed products, or sweets in students´ childhood home food environment, has been reported to be associated with consumption and preference of such products during their adolescence and young adult life 19 . Together, these factors may create environments where maintaining a healthy lifestyle is more challenging for students with a migration background. Additionally, our findings indicate that living arrangements significantly correlate with obesity risk. Students living alone or with parents exhibit higher obesity rates than those in shared flats, where shared living environments may encourage healthier eating habits 20 . Moreover, we speculate that students with obesity may find shared living arrangements uncomfortable, potentially due to fear of discrimination or stigmatisation, making shared accommodation less desirable. Some of the students are still considered to be old adolescents/ young adults, where research has found that there is a relationship between depression, obesity and poor dietary choices 7 , 21 . This reinforces the idea that there is a negative feedback loop between such factors present in our sampled population. The lower rate of shared home accommodations among students with obesity could also be attributed to social and emotional factors, such as isolation or a lack of supportive peer networks, which can negatively influence dietary choices and overall health behaviors. Consequently, students living alone or with family may rely more heavily on food delivery services, such food delivery services have been repeatedly linked with a tendency for less healthy meals 22 – 24 . Such a pattern is not as prevalent among shared flats, where communal meal planning and preparation could be more common 22 . The increased reliance on delivery services among students with obesity further highlights the impact of living arrangements on diet and underscores the importance of supportive, social living environments in promoting healthier food choices. Multiple risk factors correlate with obesity and contribute to the emerging obesity pandemic, highlighting the complexity and significance of the matter 10 , 25 . Several of the risk factors enumerated could potentially be reduced with preventive measures. Such examples are the development of Preventive educational interventions before students leave high school and transition into the university environment 26 . Such eating habit formation can have an enormous impact on the choices made in later years 26 . Moreover, it is worth mentioning the key role universities play when ensuring healthier food environments and supporting healthier habits, for example. They can be crucial in developing informative sessions, educating students about the importance of good nutrition, and providing healthy food options on campus 8 . Implications for policy and practice Transition phases (i.e. phases in life in which individuals are adapting to fundamental changes) are considered important for habit formation 27 . Leaving school and entering higher education is a critical transition phase for most students 15 . The findings from this study offer valuable insights for policies aiming to reduce obesity among university students and address associated socioeconomic and lifestyle factors. Although obesity prevalence among students is lower than in the general population, the observed increase in rates with age, along with higher risks among students from lower educational backgrounds and those with a migration background, underscores the need for targeted health interventions. Health promotion programs on campuses could specifically focus on these at-risk groups, highlighting the importance of maintaining healthy lifestyle habits and offering resources tailored to mitigate obesity risk. The observation that students with obesity are less likely to live in shared flats suggests that promoting affordable and accessible shared living spaces could support healthier lifestyles. Policies that improve student access to community or shared accommodations which might encourage positive health behaviors by fostering social support networks and healthier eating patterns within communal settings. Furthermore, since students with obesity demonstrate a higher dependency on loans, policy efforts could explore financial support and debt relief programs to alleviate economic pressures for groups with severe health conditions. Reducing financial strain may indirectly affect health behaviors by decreasing reliance on low-cost, high-calorie foods and increasing access to healthier alternatives. Finally, given that obesity trends in students mirror those of the general population, preventive and promotive health programs could be developed within university settings to encourage healthier food behavior before students enter into an obesity health condition. Universities could offer affordable gym memberships and counseling services to support students in maintaining a healthy weight and active lifestyle. In conclusion, in the setting of rising obesity prevalence, this study highlights the complex interplay of socio-demographic and lifestyle factors in the prevalence of obesity among university students in Germany. Addressing these risk factors through targeted public health interventions and policies, accounting for such heterogeneity even among students may help to mitigate the rising obesity rates in this demographic. METHODS Sampling strategy In the present analysis, we utilized data sourced from the Fachkraft 2030 survey (at the time Fachkraft 2020, just Fachkraft hereafter) 1 , conducted by Studitemps in collaboration with Maastricht University, School of Business and Economics (SBE), Department of Macro, International and Labour Economics (MILE) in 2017. The data was designed to assess the economic and general life circumstances of higher education students across Germany. The data includes a broad participant base through an invitation distributed by Studitemps via the Jobmensa network, a large job platform for students. The dataset for our study comes from the 10th wave of this biannual cross-sectional survey, which was conducted online from March 22 to April 24, 2017, using Survey-gizmo, now Alchemer, as the survey management tool. Since 2012, the survey has been conducted in 25 waves, amassing responses from more than 400,000 students up to the winter semester of 2024. The 10th wave is the first—and so far only—wave to include questions related to nutrition and physical health. As a result, earlier waves of the Fachkraft data have not been used in public health research. Previous applications have primarily focused on labor economics 28 . However, during the COVID-19 pandemic, the data also contributed to understanding students’ attitudes, emotional responses, and behavioral changes in response to the crisis, resulting in a publication aimed at a more medically oriented audience 29 . For this specific study, the initial dataset comprised raw data from 11,648 student respondents. Screening and exclusion criteria were applied to ensure the validity of the data. These criteria led to the exclusion of several subsets of respondents in the following order: 386 students (3%) were excluded for failing to report on weight or height; 11 students (0.1%) provided implausible responses regarding food preparation; 207 students (2%) were under the legal age for participation (< 18); 45 students (0.4%) reported an income exceeding €10,000 per income source, which was considered implausible; 141 students (1.2%) used less than one-third of the median response time (< 12 min), suggesting insufficient engagement with the survey; 599 students (5%) had recently completed university education; and 292 students (3%) were high school students anticipating university studies, thus not currently within the target demographic. After these exclusions, the refined dataset includes 9960 students in Germany. Representativeness of the sample An assessment of the representativeness of the Fachkraft sample reveals important insights regarding its alignment with the broader German student population, as outlined by federal statistics 30 . Historically, the Fachkraft survey data shows little systematic differences in most demographic categories compared to federal statistics, which compile pooled administrative information from universities across Germany. However, an overrepresentation of women in the survey has been observed, a common phenomenon in survey research attributed to higher response rates among women (Table 1 ). The representativeness of the sample is further illustrated with the distribution of participants by German regions (Table 1 ). Although the regions are generally well-represented, some deviations exist, particularly North Rhine-Westphalia is underrepresented (Table 1 ). The mean age of survey participants (23.4 years) is similar to the national median (no mean available), although not perfectly comparable. Additionally, there is a noted underrepresentation of students enrolled in dual-study programs by 3% percentage points, a unique aspect of the German education system (Table 1 ). Further, comparative analysis with the "Sozialerhebung 2016" 31 , a comprehensive student survey conducted every four years by the German Centre for Higher Education Research and Science Studies alongside the Studierendenwerke, validates the representativeness of other observed variables. The Fachkraft sample mirrors the "Sozialerhebung" in terms of the proportion of employed students, which is particularly relevant given that participants are recruited through Jobmensa, a job network for students (Student job, Appendix A), as well as concerning companionship and income (Appendix A). The “Sozialerhebung 2016” features more individuals living alone or with their partner (Appendix A). The sample size of 9960 students offers sufficient statistical power to analyze specific subgroups, such as students with obesity (N = 596). Data Analysis We computed Body Mass Index (BMI) based on self-reported weight and height at separate intervals throughout the survey. Our aim was to provide descriptive statistics pertaining to obesity rates. We conducted two primary types of comparisons. Firstly, we compared obesity rates, using graphical representations of mean values and 95% confidence intervals, enabling us to discern significant differences. The confidence intervals are, where necessary, explicitly mentioned in the text in brackets. Secondly, we compare cohorts of students with and without obesity graphically using mean values of multiple characteristics. To ascertain statistical significance in these cases, we employed the the Kruskal-Wallis test to address non-normal distributions. Lastly, we utilized logistical regression to elucidate heightened probabilities associated with multiple risk factors, dealing with missing values by case-wise deletion. Moreover, we employed Stata as our primary analytical tool. Through these multifaceted analyses, we aimed to offer a comprehensive understanding of the dynamics influencing obesity rates among the student population. Declarations Acknowledgments: We like to thank Studitemps for sharing this data with us free of charge and supporting this publication. Authors' contributions: Literature Review: D.L., A.I.M.E., P.v.P., Conceptualization: D.L., P.v.P., M.H., A.R., L.B., P.K.S., Methodology and Formal Analysis: D.L., Writing - Original Draft: D.L., Writing Review & Editing: P. v. P., A.I.M.E., M.H., L.B., A.R., P.K.S., D.L. Competing Interest Statements: D.L. declares to be a member of several scientific associations including EAERE (European Association of Environmental Resource Economics), EAAE (European Association of Agricultural Economics), Gewisola (German Association of Agricultural Economics), IBPPA (International Behavioral Public Policy Association). He has also received funding from the German Research foundation (DFG) and the Federal Ministry of Food and Agriculture in Germany (BMEL). PvP reports receiving research funding from Germany’s Federal Ministries of Food and Agriculture (BMEL) and Education and Research (BMBF), as well as travel costs and speaker and manuscript fees from the German and Austrian Nutrition Societies (DGE and ÖGE), the German Diabetes Society (DDG) and the German Obesity Society (DAG). The other authors declare no conflicts of interest related to this manuscript. Funding Statement: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Data Availability Statement: The data is private property of Studitemps. Studitemps has granted the right to publish this study. The data cannot be made publicly available. Via a contractual agreement the data can be shared confidentially with the Journal. Ethical Statement : This research does not involve any form of experimental manipulation or the use of deception. All procedures conducted comply with standard ethical guidelines for non-invasive, observational, or survey-based research. Participants were fully informed about the purpose of the study prior to their involvement. Informed consent was obtained from all participants, who agreed voluntarily to the terms of participation. Confidentiality and anonymity were assured, and participants retained the right to withdraw from the study at any time without penalty. 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Health - Eur. 17 , 100431 (2022). Menor-Rodriguez, M. J. et al. Influence of an Educational Intervention on Eating Habits in School-Aged Children. Children 9 , 574 (2022). Keller, M., Halkier, B. & Wilska, T.-A. Policy and Governance for Sustainable Consumption at the Crossroads of Theories and Concepts. Environ. Policy Gov. 26 , 75–88 (2016). Kiessling, L., Pinger, P., Seegers, P. & Bergerhoff, J. Gender differences in wage expectations and negotiation. Labour Econ. 87 , 102505 (2024). Asselmann, E., Borghans, L., Montizaan, R. & Seegers, P. The role of personality in the thoughts, feelings, and behaviors of students in Germany during the first weeks of the COVID-19 pandemic. PLOS ONE 15 , e0242904 (2020). DESTATIS. Bildung und Kultur - Studierende an Hochschulen Wintersemester 2016/17. Fachserie 11 4 1 (2017). Middendorff, Elke et al. Die wirtschaftliche und soziale Lage der Studierenden in Deutschland 2016. Bundesministerium für Bildung und Forschung - BMBF https://www.dzhw.eu/pdf/sozialerhebung/21/Soz21_hauptbericht_barrierefrei.pdf (2016). Tables Table 1 Sample Description and federal statistics 10. Fachkraft survey wave (2016/17) Destatis 2016/17 Age Age [years] [median]* 23 23.6 Gender Women [%] 0.55 0.48 Higher Education Research University [%] 0.58 0.63 University of Applied Sciences [%] 0.42 0.34 State of university Baden-Württemberg 0.14 0.11 Bavaria 0.14 0.15 Berlin 0.08 0.07 Brandenburg 0.02 0.02 Bremen 0.02 0.01 Hamburg 0.04 0.03 Hesse 0.09 0.10 Mecklenburg-Western Pomerania 0.01 0.02 Lower Saxony 0.07 0.09 North Rhine-Westphalia 0.23 0.31 Rhineland-Palatinate 0.04 0.05 Saarland 0.01 0.01 Saxony 0.05 0.05 Saxony-Anhalt 0.02 0.02 Schleswig-Holstein 0.03 0.02 Thuringia 0.02 0.02 University System In person program 0.96 0.93 online learning only 0.02 0.02 dual private-public 0.02 0.05 *the destatis median is calculated on a monthly basis (median = 23.6), the 10. Fachkraft calculates age on a yearly basis (mean = 23.4 years) Table 2: Odds ratios of obesity - logistic regression upon obesity status Odds ratio 95% CI Socio-demographics Age [years] 1.078*** [1.054,1.104] Gender [1= women, 0=men] 0.838* [0.703,0.998] Educational status of parent [1=no UEL or DU, 5=both parents with UEL and UD] 0.876*** [0.825,0.930] Income [€] 1.000 [1.000,1.000] Living situation - reference category shared flat with parents or relatives 2.071*** [1.570,2.733] alone or with partner 1.806*** [1.413,2.307] student housing 1.471** [1.100,1.967] sub renting 1.572* [1.006,2.457] Relationship status – reference category unmarried relationship No relationship 1.347** [1.118,1.624] married 1.510** [1.138,2.003] Food preparation mode [FFQ: 4 point Likert scale] Self-cooking 0.962 [0.849,1.091] Ready-made meals 1.013 [0.891,1.152] Canteen, restaurants 0.886 [0.775,1.014] Food delivery services 1.502*** [1.283,1.758] Parental cooked meals 0.901* [0.812,0.999] Observations 9906 Exponentiated coefficients; 95% confidence intervals in brackets., asterisks: * p < 0.05, ** p < 0.01, *** p < 0.001, FFQ= food frequency questionaire Footnotes More detailed information on the data, how it was measured and the different survey questions are available via https://jobvalley.com/pdf/2019_Studie_Fachkraft2030.pdf Additional Declarations Competing interest reported. D.L. declares to be a member of several scientific associations including EAERE (European Association of Environmental Resource Economics), EAAE (European Association of Agricultural Economics), Gewisola (German Association of Agricultural Economics), IBPPA (International Behavioral Public Policy Association). He has also received funding from the German Research foundation (DFG) and the Federal Ministry of Food and Agriculture in Germany (BMEL). PvP reports receiving research funding from Germany’s Federal Ministries of Food and Agriculture (BMEL) and Education and Research (BMBF), as well as travel costs and speaker and manuscript fees from the German and Austrian Nutrition Societies (DGE and ÖGE), the German Diabetes Society (DDG) and the German Obesity Society (DAG). The other authors declare no conflicts of interest related to this manuscript. 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Lemken","email":"data:image/png;base64,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","orcid":"","institution":"University of Bonn","correspondingAuthor":true,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Lemken","suffix":""},{"id":466305907,"identity":"a3a690fa-6e24-4ea2-8c1a-28ef33769549","order_by":1,"name":"Ana Estevez Magnasco","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Estevez","lastName":"Magnasco","suffix":""},{"id":466305908,"identity":"9fd5cea8-ca76-41cd-b715-2d74f7a7ab42","order_by":2,"name":"Monika Hartmann","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Hartmann","suffix":""},{"id":466305909,"identity":"7a99b76d-0b8c-4b74-af0a-ff203ee44135","order_by":3,"name":"Leonie Bach","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Leonie","middleName":"","lastName":"Bach","suffix":""},{"id":466305910,"identity":"b65f63aa-104a-4581-b106-c2baa0ba6a7a","order_by":4,"name":"Antje Risius","email":"","orcid":"","institution":"Fulda University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Antje","middleName":"","lastName":"Risius","suffix":""},{"id":466305911,"identity":"15161aa8-6219-4f43-8361-af5b94830ee8","order_by":5,"name":"Philipp Karl Seegers","email":"","orcid":"","institution":"University of Maastricht","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"Karl","lastName":"Seegers","suffix":""},{"id":466305912,"identity":"a9e53aae-d57c-4133-ab18-3ea88dc4dd4b","order_by":6,"name":"Peter Philipsborn","email":"","orcid":"","institution":"Ludwig-Maximilians- Universität München (LMU Munich)","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Philipsborn","suffix":""}],"badges":[],"createdAt":"2025-05-22 07:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6722133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6722133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84214285,"identity":"3990dba1-a537-4d47-9ac2-090f79d4f993","added_by":"auto","created_at":"2025-06-09 10:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61732,"visible":true,"origin":"","legend":"\u003cp\u003eUniversity students’ obesity rates in Germany\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/95cc649d7c61bf31baf4acd7.png"},{"id":84214938,"identity":"e9bca0c1-23fe-4d5a-a43c-22ea7e4fb7a3","added_by":"auto","created_at":"2025-06-09 10:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31994,"visible":true,"origin":"","legend":"\u003cp\u003eEducation of parents by university entry level (UEL) and university degree (UD)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/f5a0458166cc378d5a66d99e.png"},{"id":84214287,"identity":"955ee745-9ee0-45a5-b583-3767caf5794b","added_by":"auto","created_at":"2025-06-09 10:26:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15537,"visible":true,"origin":"","legend":"\u003cp\u003eObesity rates depending on migration status of at least 1 parent\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/b1342b689280ef2de30847f8.png"},{"id":84212438,"identity":"3f2e120f-30b8-4d8e-b330-fdd64046bd28","added_by":"auto","created_at":"2025-06-09 10:18:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23563,"visible":true,"origin":"","legend":"\u003cp\u003eAverage income and income source between students with and without obesity [in €]\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/7dd0e6a681f2c807d5ecaeee.png"},{"id":84212439,"identity":"88bc2763-7e97-4480-b3b1-2f93067b4920","added_by":"auto","created_at":"2025-06-09 10:18:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36925,"visible":true,"origin":"","legend":"\u003cp\u003eObesity rates by living situation and by relationship status\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/1a98bc87e26dc35a22cca5fd.png"},{"id":84212445,"identity":"20739dfc-626b-4c8f-ae4c-7204c86f154b","added_by":"auto","created_at":"2025-06-09 10:18:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27215,"visible":true,"origin":"","legend":"\u003cp\u003eFood preparation habits between students with obesity and non-obese students Likert Scale, ranging from 0(never) to 3(daily)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/38c78ab1437a4cfb5e13128a.png"},{"id":84212441,"identity":"51c16413-4047-4fda-8b8a-e92f20a24c1f","added_by":"auto","created_at":"2025-06-09 10:18:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32225,"visible":true,"origin":"","legend":"\u003cp\u003eFood consumption frequencies of major food groups ranging from 0(never) to 3(daily)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/6afc881957342ac2eedb164c.png"},{"id":84212449,"identity":"f0d7aad7-1df2-4fa1-8db0-bc74ebe7cd16","added_by":"auto","created_at":"2025-06-09 10:18:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":28257,"visible":true,"origin":"","legend":"\u003cp\u003eConsideration of food attributes, ranging from 0(no) to 1(yes)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/3b1611c83acac6459c5b2662.png"},{"id":84216265,"identity":"7abf55e3-76bd-48d9-93e1-abe86e669bf0","added_by":"auto","created_at":"2025-06-09 10:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1018807,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/c401ca24-8caa-4485-a189-e740d5e5042c.pdf"},{"id":84212434,"identity":"bac46e63-d768-4818-9052-6145a747bd6d","added_by":"auto","created_at":"2025-06-09 10:18:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26036,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFilesAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6722133/v1/07a390585ca23b4de6e1d376.docx"}],"financialInterests":"Competing interest reported. D.L. declares to be a member of several scientific associations including EAERE (European Association of Environmental Resource Economics), EAAE (European Association of Agricultural Economics), Gewisola (German Association of Agricultural Economics), IBPPA (International Behavioral Public Policy Association). He has also received funding from the German Research foundation (DFG) and the Federal Ministry of Food and Agriculture in Germany (BMEL). PvP reports receiving research funding from Germany’s Federal Ministries of Food and Agriculture (BMEL) and Education and Research (BMBF), as well as travel costs and speaker and manuscript fees from the German and Austrian Nutrition Societies (DGE and ÖGE), the German Diabetes Society (DDG) and the German Obesity Society (DAG). The other authors declare no conflicts of interest related to this manuscript.","formattedTitle":"Obesity, food habits and socio-demographic factors among university students in Germany: a cross-sectional study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNon-communicable diseases (NCDs), including cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes, are the leading cause of death worldwide, accounting for 71% of all deaths globally \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Key risk factors directly linked to the development of NCDs include overweight and obesity. Moreover, several risk factors contribute to the development of overweight and obesity, including unhealthy diets, low physical activity, and sedentary lifestyle behavior \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a key protagonist of chronic disease, obesity is a significant public health concern globally, with its prevalence steadily increasing in most demographics in most countries for which data are available. This steady increase also affects university students.\u003c/p\u003e \u003cp\u003eUniversity life is recognized as a critical phase where lifestyle habits formed can substantially influence long-term health outcomes. Nevertheless, there is a gap in research highlighting university student\u0026acute;s specific sociodemographic determinants of obesity. The rising incidence of obesity even among university students is concerning, due to the associated risk of chronic diseases such as diabetes, cardiovascular disease, and certain cancers. More specifically, several diseases related to obesity and overweight normally found in later stages of life, show a growing incidence among younger population groups. Such is the case with diet-related-NCD such as heart disease and early onset of type 2 diabetes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs young adults, students consolidate habits they acquired during their childhood and adolescence and develop new ones that will be present during the rest of their lives, as it has been shown for food, alcohol and nicotine consumption \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, obesity can impair not only physical health but also academic performance and overall quality of life, leading to long-term socioeconomic consequences. The transition to university life often involves changes in dietary habits regarding food preparation, physical activity levels, and stress management, all of which are crucial in impacting the risk of overweight and obesity \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. University students are susceptible, for example, to limited disposable income, restricted cooking skills, and insufficient nutrition knowledge, leading to poor food choices \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Moreover, this period can exacerbate and expose students to pre-existing or new ranges of stressors due to their transition process from adolescence to adulthood, such as academic pressure, changes in peer and family social supports and the exposure to participating in risky behaviours such as alcohol and drug use \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The implications of obesity extend beyond immediate health concerns, potentially leading to chronic diseases that affect long-term health and economic productivity. The need for integrated socio-political as well as scientific approaches to improve dietary patterns at the population level is clear \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHealth equity is a critical lens through which the issue of obesity should increasingly be examined. This also applies to Germany. Even before student life, socio-economic disparities already exist in children, where obesity rates are 9.9% among lower and 2.3% among higher socio-economic status groups based on the KIGGS panel \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In 11 years the prevalence of overweight among children from lower socioeconomic households increased significantly (from 20\u0026ndash;25.5%), while it decreased among children from higher socioeconomic status households (from 10.2 to 7.7%) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These disparities continue into adolescence. For young adults, stress plays a crucial role in the development and progression of obesity. In Germany, chronic stress is particularly associated with lower socio-economic status \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Factors such as access to nutritious food, opportunities for physical activity, and health education can be limited for all population groups, including university students, potentially exacerbating the risk of obesity \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additionally, cultural and familial influences, particularly among university students with a migration background, have been linked to obesity in some countries \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In Germany, official statistics on university students and obesity are lacking, so a representative assessment of obesity-related socioeconomic factors and dietary behaviors is still missing.\u003c/p\u003e \u003cp\u003eBy analyzing these variables, this study takes a multifaceted approach that acknowledges the heterogeneity among university students. It aims to contribute to the development of targeted public health interventions and policies that promote health equity while effectively addressing obesity within this population. Given the complex interplay of socio-demographic and lifestyle factors, this study aims to address the following research questions in the German context:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAre socio-demographic factors (age, gender, education of parents, migration status and income) associated with obesity among university students?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAre lifestyle and dietary factors (living situation, relationship status, food preparation modes, food category consumption frequencies) associated with obesity among university students?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAge and gender\u003c/h2\u003e \u003cp\u003eAmong students, obesity rates rise from 3.4% in the 18-19 age group to 9.8% in the 30 to 34 age group (Figure 1). While both women and men experience similar trends in growth of obesity prevalence with age, women consistently exhibit lower rates across all age groups. Of note, differences between population and student obesity increase from 25 years onwards.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParents: Education and Migration\u003c/h3\u003e\n\u003cp\u003e Two key formal educational qualifications are recognized in the following: firstly, the \u0026ldquo;(Fach)Abitur,\u0026rdquo; a secondary school diploma necessary for university admission in Germany (UEL); and secondly, university degrees (UD) such as Bachelor or Diploma degrees, although the former is uncommon among the parent generation in Germany. The prevalence of UEL and UD among students\u0026rsquo; parents can be accessed in the Appendix (Table A2)\u003c/p\u003e \u003cp\u003eWe see a disparity in the educational backgrounds of parents of students with obesity compared to their peers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The differences are particularly pronounced for the most and least educated groups. A university degree of both parents is linked to an obesity rate of 4% [3.1\u0026ndash;4.9] among students, which is significantly lower than students with parents with none of these formal education achievements, where the rates are at 7.8% [6.8\u0026ndash;8.5].\u003c/p\u003e \u003cp\u003eThe migration background of student families is diverse, commonly including origins such as Turkey, Russia, Poland, and Vietnam. A comparison between students with a migration background and those without shows a significant difference in obesity rates, at 5% [4.7\u0026ndash;5.8] and 8% [6.7-9.0] respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are large differences between migration origins, e.g. 12% for Turkish immigrants and 5% for Polish ones, but limited subgroup size (372 and 137 respectively) does not allow for reliable estimates.\u003c/p\u003e\n\u003ch2\u003eStudent Income\u003c/h2\u003e\n\u003cp\u003eIn Germany, students rely on four primary sources of income: a) the subsidized public student loan system (Baf\u0026ouml;g), b) private sector student loans, c) income from private individuals such as parents, relatives, friends, or partners, and d) the student job market, which encompasses all types of formalized part-time employment. Overall, there is no significant difference in total income (Kwallis p-value=0.5689) between students with obesity, who average 953\u0026euro; [878-1029], and students without obesity, who average 958\u0026euro; [939-977]. However, significant differences exist in the sources of income, with students with obesity receiving more from loans (216 [163-268]\u0026euro; vs. 138\u0026euro; [129-146\u0026euro;]) and students without obesity obtaining more from private individuals (354\u0026euro; [305-329] vs. 291\u0026euro; [194-281]) (Figure 4). Income from student jobs is similar, although students with obesity exhibit a 5 percentage points lower employment rate (Kwallis p-value= 0.0235).\u003c/p\u003e\n\u003ch3\u003eRelationship status and living situation of students\u003c/h3\u003e\n\u003cp\u003eThe primary forms of student living arrangements include residing: a. with parents or relatives, b. alone or with a partner, typically a joint household arrangement, c. in student-specific housing, d. in a shared flat, typically consisting of joint and individual household structures, e.g. through sub-renting. Sub-renting is the least common, with only 4% of students currently engaged in such arrangements. This scarcity leads to great uncertainty in estimating obesity rates for this group. Obesity rates are lowest among students living in shared flats, at 3.9% [3.1\u0026ndash;4.5], whereas the highest rates, ranging from 7.1% [6.0-8.1] to 7.5% [6.5\u0026ndash;8.5], occur among students living with parents, relatives, alone, or with a partner (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, significant differences in obesity rates correlate with relationship status; unmarried students in relationships exhibit the lowest rates with 5.2% [4.5\u0026ndash;5.8], while married students show the highest 8.3% [6.5\u0026ndash;10.1] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Married students are also the oldest group with an average age of 24.4 years compared to 22.9 and 23.4 for no relationship and unmarried relationship. 2.3% of all students have children; 12% [7.8\u0026ndash;16.4] of students with children have obesity, but the small number of cases precludes a reliable estimate.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFood habits of students\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe examine how often students engage in cooking for themselves, dining at canteens and restaurants, consuming meals prepared by parents or relatives, consuming ready-made meals, and ordering from food delivery services. Inquiring about the frequency with which students prepare or obtain food through these channels\u0026mdash;ranging from never to once per week or less, multiple times a week, to daily\u0026mdash;reveals that students with and without obesity have on average similar habits, with the exception of food delivery services. Specifically, a Kruskal-Wallis test indicates no significant differences in the use of ready-made meals (p-value: 0.1427) or any other food acquisition methods. However, students with obesity significantly order more often from delivery services (p-value=0.0001) with a frequency score of 0.74[0.70-0.79] vs. 0.62 [0-61-0.63] (Figure 6).\u003c/p\u003e \u003cp\u003eSimilarly, the frequency of consumption across various food categories is largely comparable between students with and without obesity. The sole significant difference is in meat consumption, where there is a large variance, with students with obesity averaging 1.9 [1.79\u0026ndash;1.93] compared to 1.6 [1.58\u0026ndash;1.62] for students without obesity on a 4-point Likert scale (p-value: 0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsumer interest in food product attributes differs significantly between attributes commonly labelled, namely organic, fair-trade, regional and vegetarian products. When asked whether consumer consider these attributes during food purchases, students with obesity are less likely to consider them. The implications of such consumer behavior are not clear at this point (Figure 8).\u003c/p\u003e\n\u003ch3\u003eProbabilities in a multivariate analysis\u003c/h3\u003e\n\u003cp\u003e \u003cp\u003eWe conducted a multivariate analysis that incorporates various factors associated with obesity into a single regression model to account for interdependencies. For instance, this model assesses whether being married remains a risk factor when controlling for age, gender and other factors. It includes a range of previously introduced variables such as socio-demographic characteristics, living situations, relationship status, and food preparation modes. A confidence interval that does not include 1 indicates a significant effect at an alpha 5% level, meaning the relative chance of obesity changes with the associated factor. For example, odds ratios (OR) of 2 suggest a doubled chance of having obesity, as seen in individuals living with parents or relatives compared to those living in shared flats, while an OR of 0.5 indicates a halved chance. The independent variables are non-standardized, which means that marginal effect sizes, expressed as odds ratios (ORs), are not directly comparable but are more straightforward to interpret. For instance, each additional year of student age is associated, on average, with a 1.078 times higher likelihood of obesity, while each additional formal education degree attained by parents is associated with a 0.876 times lower likelihood of obesity. Multiplying effects, the results show that a student living with parents (OR\u0026thinsp;=\u0026thinsp;2.071), ordering frequently from delivery services (OR\u0026thinsp;=\u0026thinsp;1.502), and without a claimed relationship (OR\u0026thinsp;=\u0026thinsp;1.347) has a fourfold increased probability of having obesity (cumulative OR\u0026thinsp;=\u0026thinsp;4.2).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e \u003cem\u003eMain Findings\u003c/em\u003e: This study aimed to investigate the multifaceted nature of obesity among university students in Germany, with a particular focus on socioeconomic, sociodemographic, lifestyle and dietary factors. The findings provide critical insights into the underlying factors associated with obesity in this demographic and the factors not associated. Such factors must not be causally linked to obesity but can be used to identify groups at higher risk for obesity, which may help to inform health promotion efforts.\u003c/p\u003e \u003cp\u003eObesity is less prevalent among university students in Germany compared to the general population, especially after the age 25 is reached and with a slightly higher occurrence among men. The gender difference in obesity prevalence is smaller in the student population than in the general population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although obesity is less prevalent among students compared to the general population, the increase in students\u0026rsquo; obesity rates with age seen in our study mirrors that of the broader population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudents whose parents both hold university degrees have a lower prevalence of obesity compared to those whose parents did not pass a university entry examination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, students with a migration background are more likely to experience obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Monthly income does not significantly differ between students with and without obesity; however, students with obesity are more dependent on loans (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Students with obesity are also less likely to live in shared flats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), order from food delivery services more frequently (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and place less emphasis on labelled food attributes (Fig.\u0026nbsp;8). Furthermore, they consume meat more often than students without obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The here mentioned factors remain significantly correlated with obesity even when included together in a joint model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStrength and Limitations\u003c/strong\u003e \u003cp\u003eOur study possesses several relevant strengths. To our knowledge, it is the only study to provide detailed data on obesity prevalence and relevant determinants specifically among university students in Germany. The sample is large and representative of the German university student population concerning state of residence, employment status, income, type of institution, and term of enrollment, enhancing generalizability. Data collection was supported by multiple quality assurance measures, contributing to the robustness of findings.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThis study also has several limitations. First, it relies on self-reported data for weight and height, which may introduce reporting biases. Second, we use BMI as the primary criterion for defining overweight and obesity, despite recognized limitations in its ability to reflect overall health accurately. Finally, the study\u0026rsquo;s cross-sectional design restricts our capacity to infer causality between the identified determinants and obesity, limiting conclusions to associations rather than cause-and-effect relationships.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInterpretation\u003c/strong\u003e \u003cp\u003eThe findings from this study reveal nuanced associations between socioeconomic and sociodemographic factors, parental background, living arrangements, and obesity risk among university students in Germany. Unlike in previous research \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, we did not find a positive relationship between lower household income and obesity risk; however, in this study, this risk manifests specifically among students reliant on loans. This loan dependency could contribute to elevated stress levels due to financial pressures, as students face the dual burden of repaying loans while aiming to complete their studies within a limited timeframe. Reliance on parental support may provide financial stability without the associated stress. Heightened stress from financial constraints may, therefore, exacerbate obesity risk, aligning with findings that stress can impact eating habits and weight gain \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eParental education level also appears to play a key role, emphasizing the impact of early-life socio-economic conditions on obesity risk, even in a relatively young and educated demographic. This finding highlights the influence of parental education as a lasting factor that may shape health behaviors and lifestyle choices into adulthood. Similarly, the migration background of students is linked with higher obesity rates, supporting evidence from previous research that students with migrant parents are at increased risk \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This association could reflect various influences, including cultural dietary patterns, socioeconomic challenges that migrant families may face, and possibly limited access to health-promoting resources in neighborhoods with higher migrant populations. Other factors can be a lack of family resources in terms of knowledge and to provide healthy foods at home \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Food accessibility of culturally appropriate ultra-processed products, or sweets in students\u0026acute; childhood home food environment, has been reported to be associated with consumption and preference of such products during their adolescence and young adult life \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Together, these factors may create environments where maintaining a healthy lifestyle is more challenging for students with a migration background.\u003c/p\u003e \u003cp\u003eAdditionally, our findings indicate that living arrangements significantly correlate with obesity risk. Students living alone or with parents exhibit higher obesity rates than those in shared flats, where shared living environments may encourage healthier eating habits \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Moreover, we speculate that students with obesity may find shared living arrangements uncomfortable, potentially due to fear of discrimination or stigmatisation, making shared accommodation less desirable. Some of the students are still considered to be old adolescents/ young adults, where research has found that there is a relationship between depression, obesity and poor dietary choices \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This reinforces the idea that there is a negative feedback loop between such factors present in our sampled population.\u003c/p\u003e \u003cp\u003eThe lower rate of shared home accommodations among students with obesity could also be attributed to social and emotional factors, such as isolation or a lack of supportive peer networks, which can negatively influence dietary choices and overall health behaviors. Consequently, students living alone or with family may rely more heavily on food delivery services, such food delivery services have been repeatedly linked with a tendency for less healthy meals \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Such a pattern is not as prevalent among shared flats, where communal meal planning and preparation could be more common \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The increased reliance on delivery services among students with obesity further highlights the impact of living arrangements on diet and underscores the importance of supportive, social living environments in promoting healthier food choices. Multiple risk factors correlate with obesity and contribute to the emerging obesity pandemic, highlighting the complexity and significance of the matter \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral of the risk factors enumerated could potentially be reduced with preventive measures. Such examples are the development of Preventive educational interventions before students leave high school and transition into the university environment \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Such eating habit formation can have an enormous impact on the choices made in later years\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Moreover, it is worth mentioning the key role universities play when ensuring healthier food environments and supporting healthier habits, for example. They can be crucial in developing informative sessions, educating students about the importance of good nutrition, and providing healthy food options on campus \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplications for policy and practice\u003c/strong\u003e \u003cp\u003eTransition phases (i.e. phases in life in which individuals are adapting to fundamental changes) are considered important for habit formation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Leaving school and entering higher education is a critical transition phase for most students \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The findings from this study offer valuable insights for policies aiming to reduce obesity among university students and address associated socioeconomic and lifestyle factors. Although obesity prevalence among students is lower than in the general population, the observed increase in rates with age, along with higher risks among students from lower educational backgrounds and those with a migration background, underscores the need for targeted health interventions. Health promotion programs on campuses could specifically focus on these at-risk groups, highlighting the importance of maintaining healthy lifestyle habits and offering resources tailored to mitigate obesity risk.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe observation that students with obesity are less likely to live in shared flats suggests that promoting affordable and accessible shared living spaces could support healthier lifestyles. Policies that improve student access to community or shared accommodations which might encourage positive health behaviors by fostering social support networks and healthier eating patterns within communal settings. Furthermore, since students with obesity demonstrate a higher dependency on loans, policy efforts could explore financial support and debt relief programs to alleviate economic pressures for groups with severe health conditions. Reducing financial strain may indirectly affect health behaviors by decreasing reliance on low-cost, high-calorie foods and increasing access to healthier alternatives. Finally, given that obesity trends in students mirror those of the general population, preventive and promotive health programs could be developed within university settings to encourage healthier food behavior before students enter into an obesity health condition. Universities could offer affordable gym memberships and counseling services to support students in maintaining a healthy weight and active lifestyle.\u003c/p\u003e \u003cp\u003eIn conclusion, in the setting of rising obesity prevalence, this study highlights the complex interplay of socio-demographic and lifestyle factors in the prevalence of obesity among university students in Germany. Addressing these risk factors through targeted public health interventions and policies, accounting for such heterogeneity even among students may help to mitigate the rising obesity rates in this demographic.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSampling strategy\u003c/h2\u003e \u003cp\u003eIn the present analysis, we utilized data sourced from the Fachkraft 2030 survey (at the time Fachkraft 2020, just Fachkraft hereafter)\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e, conducted by Studitemps in collaboration with Maastricht University, School of Business and Economics (SBE), Department of Macro, International and Labour Economics (MILE) in 2017. The data was designed to assess the economic and general life circumstances of higher education students across Germany. The data includes a broad participant base through an invitation distributed by Studitemps via the Jobmensa network, a large job platform for students. The dataset for our study comes from the 10th wave of this biannual cross-sectional survey, which was conducted online from March 22 to April 24, 2017, using Survey-gizmo, now Alchemer, as the survey management tool. Since 2012, the survey has been conducted in 25 waves, amassing responses from more than 400,000 students up to the winter semester of 2024. The 10th wave is the first\u0026mdash;and so far only\u0026mdash;wave to include questions related to nutrition and physical health. As a result, earlier waves of the Fachkraft data have not been used in public health research. Previous applications have primarily focused on labor economics \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, during the COVID-19 pandemic, the data also contributed to understanding students\u0026rsquo; attitudes, emotional responses, and behavioral changes in response to the crisis, resulting in a publication aimed at a more medically oriented audience \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor this specific study, the initial dataset comprised raw data from 11,648 student respondents. Screening and exclusion criteria were applied to ensure the validity of the data. These criteria led to the exclusion of several subsets of respondents in the following order: 386 students (3%) were excluded for failing to report on weight or height; 11 students (0.1%) provided implausible responses regarding food preparation; 207 students (2%) were under the legal age for participation (\u0026lt;\u0026thinsp;18); 45 students (0.4%) reported an income exceeding \u0026euro;10,000 per income source, which was considered implausible; 141 students (1.2%) used less than one-third of the median response time (\u0026lt;\u0026thinsp;12 min), suggesting insufficient engagement with the survey; 599 students (5%) had recently completed university education; and 292 students (3%) were high school students anticipating university studies, thus not currently within the target demographic. After these exclusions, the refined dataset includes 9960 students in Germany.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eRepresentativeness of the sample\u003c/h3\u003e\n\u003cp\u003eAn assessment of the representativeness of the Fachkraft sample reveals important insights regarding its alignment with the broader German student population, as outlined by federal statistics \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Historically, the Fachkraft survey data shows little systematic differences in most demographic categories compared to federal statistics, which compile pooled administrative information from universities across Germany. However, an overrepresentation of women in the survey has been observed, a common phenomenon in survey research attributed to higher response rates among women (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe representativeness of the sample is further illustrated with the distribution of participants by German regions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the regions are generally well-represented, some deviations exist, particularly North Rhine-Westphalia is underrepresented (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of survey participants (23.4 years) is similar to the national median (no mean available), although not perfectly comparable. Additionally, there is a noted underrepresentation of students enrolled in dual-study programs by 3% percentage points, a unique aspect of the German education system (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, comparative analysis with the \"Sozialerhebung 2016\" \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, a comprehensive student survey conducted every four years by the German Centre for Higher Education Research and Science Studies alongside the Studierendenwerke, validates the representativeness of other observed variables. The Fachkraft sample mirrors the \"Sozialerhebung\" in terms of the proportion of employed students, which is particularly relevant given that participants are recruited through Jobmensa, a job network for students (Student job, Appendix A), as well as concerning companionship and income (Appendix A). The \u0026ldquo;Sozialerhebung 2016\u0026rdquo; features more individuals living alone or with their partner (Appendix A). The sample size of 9960 students offers sufficient statistical power to analyze specific subgroups, such as students with obesity (N\u0026thinsp;=\u0026thinsp;596).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eWe computed Body Mass Index (BMI) based on self-reported weight and height at separate intervals throughout the survey. Our aim was to provide descriptive statistics pertaining to obesity rates. We conducted two primary types of comparisons. Firstly, we compared obesity rates, using graphical representations of mean values and 95% confidence intervals, enabling us to discern significant differences. The confidence intervals are, where necessary, explicitly mentioned in the text in brackets. Secondly, we compare cohorts of students with and without obesity graphically using mean values of multiple characteristics. To ascertain statistical significance in these cases, we employed the the Kruskal-Wallis test to address non-normal distributions. Lastly, we utilized logistical regression to elucidate heightened probabilities associated with multiple risk factors, dealing with missing values by case-wise deletion. Moreover, we employed Stata as our primary analytical tool. Through these multifaceted analyses, we aimed to offer a comprehensive understanding of the dynamics influencing obesity rates among the student population.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe like to thank Studitemps for sharing this data with us free of charge and supporting this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eLiterature Review: D.L., A.I.M.E., P.v.P., Conceptualization: D.L., P.v.P., M.H., A.R., L.B., P.K.S., Methodology and Formal Analysis: D.L., Writing - Original Draft: D.L., Writing Review \u0026amp; Editing: P. v. P., A.I.M.E., M.H., L.B., A.R., P.K.S., D.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statements:\u0026nbsp;\u003c/strong\u003eD.L. declares to be a member of several scientific associations including EAERE (European Association of Environmental Resource Economics), EAAE (European Association of Agricultural Economics), Gewisola (German Association of Agricultural Economics), IBPPA (International Behavioral Public Policy Association). He has also received funding from the German Research foundation (DFG) and the Federal Ministry of Food and Agriculture in Germany (BMEL).\u0026nbsp;PvP reports receiving research funding from Germany\u0026rsquo;s Federal Ministries of Food and Agriculture (BMEL) and Education and Research (BMBF), as well as travel costs and speaker and manuscript fees from the German and Austrian Nutrition Societies (DGE and \u0026Ouml;GE), the German Diabetes Society (DDG) and the German Obesity Society (DAG).\u0026nbsp;The other authors declare no conflicts of interest related to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data is private property of Studitemps. Studitemps has granted the right to publish this study. The data cannot be made publicly available. Via a contractual agreement the data can be shared confidentially with the Journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e: This research does not involve any form of experimental manipulation or the use of deception. All procedures conducted comply with standard ethical guidelines for non-invasive, observational, or survey-based research. Participants were fully informed about the purpose of the study prior to their involvement. Informed consent was obtained from all participants, who agreed voluntarily to the terms of participation. Confidentiality and anonymity were assured, and participants retained the right to withdraw from the study at any time without penalty. The study was designed to minimize any potential risks and adheres to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBudreviciute, A. \u003cem\u003eet al.\u003c/em\u003e Management and Prevention Strategies for Non-communicable Diseases (NCDs) and Their Risk Factors. \u003cem\u003eFront. Public Health\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 574111 (2020).\u003c/li\u003e\n\u003cli\u003eGlobal Burden of Disease Collaborative Network. \u003cem\u003eGlobal Burden of Disease Study 2019\u003c/em\u003e. https://vizhub.healthdata.org/gbd-results/ (2020).\u003c/li\u003e\n\u003cli\u003eAfshin, A., Micha, R., Khatibzadeh, S., Schmidt, L. A. \u0026amp; Mozaffarian, D. Dietary Policies to Reduce Non-Communicable Diseases. in \u003cem\u003eThe Handbook of Global Health Policy\u003c/em\u003e (eds. Brown, G. 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J. \u003cem\u003eet al.\u003c/em\u003e Influence of an Educational Intervention on Eating Habits in School-Aged Children. \u003cem\u003eChildren\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 574 (2022).\u003c/li\u003e\n\u003cli\u003eKeller, M., Halkier, B. \u0026amp; Wilska, T.-A. Policy and Governance for Sustainable Consumption at the Crossroads of Theories and Concepts. \u003cem\u003eEnviron. Policy Gov.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 75\u0026ndash;88 (2016).\u003c/li\u003e\n\u003cli\u003eKiessling, L., Pinger, P., Seegers, P. \u0026amp; Bergerhoff, J. Gender differences in wage expectations and negotiation. \u003cem\u003eLabour Econ.\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 102505 (2024).\u003c/li\u003e\n\u003cli\u003eAsselmann, E., Borghans, L., Montizaan, R. \u0026amp; Seegers, P. The role of personality in the thoughts, feelings, and behaviors of students in Germany during the first weeks of the COVID-19 pandemic. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e0242904 (2020).\u003c/li\u003e\n\u003cli\u003eDESTATIS. Bildung und Kultur - Studierende an Hochschulen Wintersemester 2016/17. \u003cem\u003eFachserie 11 4 1\u003c/em\u003e (2017).\u003c/li\u003e\n\u003cli\u003eMiddendorff, Elke \u003cem\u003eet al.\u003c/em\u003e Die wirtschaftliche und soziale Lage der Studierenden in Deutschland 2016. \u003cem\u003eBundesministerium f\u0026uuml;r Bildung und Forschung - BMBF\u003c/em\u003e https://www.dzhw.eu/pdf/sozialerhebung/21/Soz21_hauptbericht_barrierefrei.pdf (2016).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Sample Description and federal statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10. Fachkraft survey wave \u0026nbsp; (2016/17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDestatis 2016/17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge [years] [median]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigher Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResearch University \u0026nbsp;[%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUniversity of Applied Sciences \u0026nbsp;[%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eState of university\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBaden-W\u0026uuml;rttemberg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBavaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBerlin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBrandenburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBremen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHamburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHesse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMecklenburg-Western Pomerania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLower Saxony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNorth Rhine-Westphalia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRhineland-Palatinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSaarland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSaxony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSaxony-Anhalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSchleswig-Holstein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eThuringia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity System\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn person program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eonline learning only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edual private-public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*the destatis median is calculated on a monthly basis (median = 23.6), the 10. Fachkraft calculates age on a yearly basis (mean = 23.4 years)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Odds ratios of obesity - logistic regression upon obesity status\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33.3139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8992%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eSocio-demographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eAge [years]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.054,1.104]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eGender [1= women, 0=men]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e0.838*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.703,0.998]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eEducational status of parent [1=no UEL or DU, 5=both parents with UEL and UD]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e0.876***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.825,0.930]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eIncome [\u0026euro;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.000,1.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eLiving situation - reference category shared flat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003ewith parents or relatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e2.071***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.570,2.733]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003ealone or with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.806***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.413,2.307]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003estudent housing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.471**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.100,1.967]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003esub renting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.572*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.006,2.457]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eRelationship status \u0026ndash; reference category unmarried relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eNo relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.347**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.118,1.624]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.510**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.138,2.003]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eFood preparation mode [FFQ: 4 point Likert scale]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eSelf-cooking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.849,1.091]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eReady-made meals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.891,1.152]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eCanteen, restaurants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.775,1.014]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eFood delivery services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e1.502***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[1.283,1.758]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eParental cooked meals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4147%;\"\u003e\n \u003cp\u003e0.901*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7451%;\"\u003e\n \u003cp\u003e[0.812,0.999]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61.4382%;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33.3139%;\"\u003e\n \u003cp\u003e9906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eExponentiated coefficients; 95% confidence intervals in brackets., asterisks:\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, FFQ= food frequency questionaire\u003c/p\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e More detailed information on the data, how it was measured and the different survey questions are available via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jobvalley.com/pdf/2019_Studie_Fachkraft2030.pdf\u003c/span\u003e\u003cspan address=\"https://jobvalley.com/pdf/2019_Studie_Fachkraft2030.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Overweight, University students Socio-cultural factors, Risk factors, Population health, Living conditions, Dietary Patterns, Financial dependency","lastPublishedDoi":"10.21203/rs.3.rs-6722133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6722133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity represents a growing public health challenge globally, with rising prevalence among university students. Its causes extend beyond diet and exercise, encompassing socioeconomic background, experiences of discrimination, and family education and migration history. This study examined obesity among German university students using data from the 2016 Studitemps survey, which included over 9,960 participants and mirrors key characteristics of national statistics and the largest social study of German students \u0026ldquo;Sozialerhebung 2016\u0026rdquo;. Findings reveal that obesity prevalence was higher among male students and increased with age, ranging from 3.4% in the youngest group to 9.8% in those aged 30\u0026ndash;34. Students with less-educated parents or with a migration background showed notably higher obesity rates. Additionally, those living alone, relying on student loans, or frequently using food delivery services were more likely to be obese, while those living in shared flats had lower rates. Public and university initiatives should focus on at-risk groups by improving access to shared accommodations, promoting healthier food choices to reduce reliance on delivery services, and addressing cultural challenges faced by students from migrant or lower-education households. In conclusion, this study highlights the complex interplay of socio-demographic and lifestyle factors in obesity among university students.\u003c/p\u003e","manuscriptTitle":"Obesity, food habits and socio-demographic factors among university students in Germany: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 10:18:40","doi":"10.21203/rs.3.rs-6722133/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T11:28:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T00:32:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183083455263172689876978285777504240137","date":"2025-08-21T01:53:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261640402890256778650134776575523590008","date":"2025-06-19T02:20:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T21:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71950113188994020226990085720567793853","date":"2025-06-08T19:48:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T10:52:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-03T10:48:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-03T06:03:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-03T03:48:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-22T07:06:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a7689d4-adcf-4531-87f4-45a00e241243","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49504169,"name":"Health sciences/Risk factors"},{"id":49504170,"name":"Biological sciences/Physiology/Ageing"}],"tags":[],"updatedAt":"2026-03-24T10:54:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 10:18:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6722133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6722133","identity":"rs-6722133","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}