Is health behavior among university students determined by actual body shape or by body image? A European study

Abstract

BACKGROUND:

Appropriate body image self-perceptions provide a good help to increase the feeling of personal well-being, thus having an important impact on health. Universities, having an important role in shaping of the future workers, represent an important setting to approach health issues.

OBJECTIVE:

This study determined to what extent different types of students in higher education (four categories of students were created: “self-secure”, “perfect”, “destructive” and “apologetic”) are likely to adopt different health risk behaviors.

METHODS:

A cross-sectional study in a sample of students from five European Universities in the 2016/2017 academic year was conducted. Based on the combination of body image perception and body mass index, four types of students were identified: “self-secure” (overweight students with a good self-esteem); “perfect” (underweight students with a good self-esteem); “destructive” (overweight students with a poor self-esteem); “apologetic” (underweight students with a poor self-esteem).

RESULTS:

The study reveals that the defined types of students differed in terms of risk behavior. When the control was included, the “self-secure” student type had a reduced likelihood of being on a diet (22.3%) and physically active (17.8%) than other students (p < 0.001).

CONCLUSIONS:

The results of this paper raise concerns about the future because the body dissatisfaction of the college student could be a big impact in long term whether at collective, personally or even professionally.

Keywords

Lifestyle self-perception health risk behaviors graduate education

1 Background

Body image plays an important role in self-perception of health status and how people shape their health behavior [1]. Society has an influential role in individuals’ perception of their body image, and there is a complex relationship between internal (personal) and external (society) perception [2]. Studies have shown that there might be considerable discrepancy between how individuals perceive the body visually and how it would be perceived objectively using constructed measures of body fat indicators, such as the body mass index (BMI) [3, 4].

In particular, large discrepancies have been observed among women and students from advantaged parental backgrounds [5] when compared with men and students from disadvantaged backgrounds [6]. However, it would appear that most individuals, besides the perceived and the measured weight, have a desired weight [7]. As a result, it is relevant to study the relationship between self-perception of weight, desired weight, and real weight. The mismatch among the objectively measured, self-perceived and desired BMI may play a significant role in the obesity epidemic, by obfuscating an individual’s actual health state, in turn, inhibiting the appropriate and necessary behavioral and lifestyle changes vital to safeguarding the well-being of both citizens and societies.

Universities have an important role in shaping the knowledge, skills and competencies of the future generation, and thereby, of futures workers and future society [8, 9]. Students in higher educational facilities can be expected to be the decision-makers in the future. As such, their beliefs and attitudes, as well as behavior regarding health, are important factors that influence not only individuals, but can be expected to have an impact of tomorrow’s society.

In this study, we addressed the following questions: (i) what do young people in higher education think about health behavior, body self-esteem and BMI? and (ii) how are these interrelated? We hypothesised that risky health behavior (personal predisposition to contract diseases) could be predicted by student type. Four different student types were constructed based on combinations of body shape (BMI) and body image perception.

To test the hypothesis, we first explore the different aspects of health behavior among university students at higher education institutes. Second, we investigate the interrelation between health behavior, body self-esteem and BMI among university students. In light of the findings, we discuss policy implications, including the need for a training package for university teachers, tools for usage in higher education, and policy recommendations to decision-makers and educational planners.

2 Methods

This study was informed by an international survey carried out in 2016, in higher education facilities, in which the authors, members of one of the working groups of European Cooperation in Science and Technology (COST) Action IS1210 Appearance Matters, had access to a well-defined group of students in each of several different educational programs. Each participating country was responsible for conducting a survey. This research was reviewed and approved by the ethical committees of each of the five locations of collected data: University of Chemistry and Technology, Prague, Czech Republic; University of Zagreb, Croatia; University of Applied Sciences and Arts, Dortmund; Aalborg University, Denmark; University of Aveiro, Portugal (Process 5 to 9/2016) and written consent was obtained from all participants students. The participants (N = 980) included 163 students from Denmark, 105 students from Croatia, 157 students from Portugal, 299 students from the Czech Republic and 256 students from Germany.

The different educational programmes were categorised, according to the World University Ranking [10]. This typology was divided into Humanity and Social Sciences (Art and Humanity, Social Sciences and Management), and Nature and Technical Sciences (Engineering and Technology, Life Sciences and Medicine, Nature Sciences), resulting in 369 students studying Humanity and Social Sciences, and 611 students studying Nature and Technical Sciences.

Overall (N = 980), considering that we didn’t have complete information in BESAA, BMI and income variables the final sample of this study includes 739 students.

2.1 Instruments

Data on demographic and behavioral characteristics were collected by a questionnaire that contained questions on alcohol, tobacco use, sexual behavior, internet, as well as eating behavior, smoking and physical activity. The questions were based on the “Health Behavior in School-aged Children” (HBSC) survey [11 –13] and adapted to our population.

Body image was captured using the Body Esteem Scale (BES) that includes 23 items [14]. The descriptive analysis of the items of the BES about all responses by participants can see in Table 1. For each item, respondents rate their degree of agreement with each statement along a 5-point Likert scale ranging from 1 (strongly agree) to 5 (strongly disagree). Cronbach’s alpha for the overall BES was 0.9227.

Table 1
Descriptive of the items of the Body Esteem Scale (N = 739)

Outcome range Outcome Mean (SD)

Body image Cronbach’s alpha = 0.9227 0–92 0.4914 (0.1678)

I don’t like what I look like in the picture 0–4 1.8573 (0.8661)

Others don’t consider me good looking 0–4 1.5101 (0.7911)

I’m not proud of my body 0–4 1.8921 (1.0782)

I am preoccupied with trying to change my body weight 0–4 1.8494 (1.2239)

I think my appearance would stop me from getting a job 0–4 2.2662 (1.1637)

I don’t like what I see when I look in the mirror 0–4 1.6595 (0.9122)

There are a lot of things I’d change about my look if I could 0–4 1.8561 (1.1625)

I am not satisfied with my weight 0–4 1.8617 (1.2359)

I wish I looked better 0–4 2.0303 (1.1619)

I really don’t like what I weigh 0–4 2.1842 (1.2619)

I wish I looked like someone else 0–4 1.0622 (1.1126)

People my own age don’t like my look 0–4 1.6528 (0.8614)

My looks upset me 0–4 0.9865 (1.0292)

I am not as nice looking as most people 0–4 1.5876 (0.9981)

I am not really happy about the way I look 0–4 1.1421 (0.9524)

I don’t feel I weigh the right amount for my height 0–4 1.7539 (1.2784)

I feel ashamed of how I look 0–4 0.7932 (1.0005)

Weighing myself depresses me 0–4 1.1719 (1.2774)

My weight makes me unhappy 0–4 1.0719 (1.1077)

My looks stop me from getting dates 0–4 2.1146 (1.2084)

I worry about the way I look 0–4 1.2741 (1.2069)

I don’t think I have a good body 0–4 1.7797 (1.1056)

I don’t look as nice I’d like to 0–4 1.9325 (1.1011)

Outcome range	Outcome	Mean (SD)
Body image Cronbach’s alpha = 0.9227	0–92	0.4914 (0.1678)
I don’t like what I look like in the picture	0–4	1.8573 (0.8661)
Others don’t consider me good looking	0–4	1.5101 (0.7911)
I’m not proud of my body	0–4	1.8921 (1.0782)
I am preoccupied with trying to change my body weight	0–4	1.8494 (1.2239)
I think my appearance would stop me from getting a job	0–4	2.2662 (1.1637)
I don’t like what I see when I look in the mirror	0–4	1.6595 (0.9122)
There are a lot of things I’d change about my look if I could	0–4	1.8561 (1.1625)
I am not satisfied with my weight	0–4	1.8617 (1.2359)
I wish I looked better	0–4	2.0303 (1.1619)
I really don’t like what I weigh	0–4	2.1842 (1.2619)
I wish I looked like someone else	0–4	1.0622 (1.1126)
People my own age don’t like my look	0–4	1.6528 (0.8614)
My looks upset me	0–4	0.9865 (1.0292)
I am not as nice looking as most people	0–4	1.5876 (0.9981)
I am not really happy about the way I look	0–4	1.1421 (0.9524)
I don’t feel I weigh the right amount for my height	0–4	1.7539 (1.2784)
I feel ashamed of how I look	0–4	0.7932 (1.0005)
Weighing myself depresses me	0–4	1.1719 (1.2774)
My weight makes me unhappy	0–4	1.0719 (1.1077)
My looks stop me from getting dates	0–4	2.1146 (1.2084)
I worry about the way I look	0–4	1.2741 (1.2069)
I don’t think I have a good body	0–4	1.7797 (1.1056)
I don’t look as nice I’d like to	0–4	1.9325 (1.1011)

Notes: Every answer ranks from never, rare, seldom, often and always. n = 890. SD, standard deviation.

The Vulnerable Attachment Style Questionnaire (VASQ) [15] was used to explore insecure attachment styles in relation to dislike of body image. The VASQ includes 22 items on a 5-point Likert scale (5 = strongly agree, 4 = agree, 3 = unsure, 2 = disagree, 1 = strongly disagree). Higher scores indicate a more vulnerable attachment when computing a total score. Cronbach’s alpha for the overall VASQ was 0.79.

2.2 Data

The data set used to investigate the association between different student types and risky behavior is a part of the COST Action IS1210 Appearance Matters described above. We identified four dependent variables that capture students’ risk behavior. These variables, along with their codes, included: smoking status, captured by the question “How often do you smoke?” (1 = every day, 0 = otherwise); binge drinking, captured by the question “On how many occasions (if any) in the last 30 days have you had five or more drinks on one occasion?” (1 = at least 2–3 times a week, 0 = otherwise); physically active, captured by the question “Do you do sport regularly?” (1 = at least 2–3 times a week, 0 = otherwise); dieting, captured by the question “Do you control your diet?” (1 = yes, 0 = no).

The final English version of the instruments was used to design a web-based questionnaire (WBQ). National contact points and local survey sample coordinator’s (LSSC’s) were offered the option to translate it into their respective national language and do their own data collection in the respective language and included the option of using paper-published questionnaires. The inclusion criteria in the study were as follows: a contact person (teacher) that will commit to assist and allow time (30 min estimation) for students to fill out the survey (WBQ or printed) under the supervision of the LSSC; a contact person (teacher) that will commit to make sure that a threshold of 50%response rate will be reached; a sample of at least 100 students in higher education (either undergraduate or graduate).

Both the BES scale and BMI were standardised to have a span of 0–1. Thus, we generated the variables underweight (equal to 1 if BMI ≤ 18.5 kg/m² and 0 otherwise) and overweight (equal to 1 if BMI ≥25 kg/m² and 0 otherwise).

Based on the assumed interplay between body image and social attachment style, four different student types were constructed, according to our independent variable captures. The four student types included: (i) “self-secure”, if overweight and with a good self-esteem; (ii)“perfect”, if underweight and with a good self-esteem; (iii)“destructive”, if overweight with a poor self-esteem; (iv)“apologetic”, if underweight with a poor self-esteem.

2.3 Analysis

In this article, our main regressions focused on the impact of different types of students on risky health behavior.

Our simple ordinary least squares (OLS) regression is: $\begin{matrix} RB = β_{0} + β_{1} S + β_{3} X \\ RB = β_{0} + β_{1} P + β_{3} X \\ RB = β_{0} + β_{1} D + β_{3} X \\ RB = β_{0} + β_{1} A + β_{3} X \end{matrix}$

Where RB is the four different type of risk behaviour. S if the self-secure students’ type. P is the perfect students’ type. D is the destructive students’ type and A is the apologetic students’ type. Each of the four model are run as a separate regression for each risky behaviour. Furthermore, to explore any heterogeneous impact, a sub-group analysis was performed, capturing the impact for, respectively, women and men, and for students from high- and low-income families. In both cases, the OLS coefficients mask heterogeneous effects of different types of students on risky health behaviour if gender or parental income have a reinforcing or modifying effect.

3 Results

In this sample of students from five European Universities, 493 (67%) were women, and most of the total students (69%) reported their parents as having a high income.

Table 2 shows to what extent, respectively, students who smoke, binge drink, diet or are active differ according to the independent variables “self-secure”, “destructive”, “perfect” and “apologetic”. We likewise considered whether they differed according to the control variables. Students that were “self-secure” were found to be significantly less prone to be dieting and physically active compared with the full sample (2.72%and 3.43%versus 6.90%, respectively).

Table 2
Descriptive statistics of the sample (N = 739)

All n (%) Smoke n (%) Binge drink n (%) Diet n (%) Active n (%)

Self-secure 0.0690 0.0659 0.0872 0.0272^* 0.0343^

(0.2536) (0.2486) (0.2827) (0.1631) (0.1910)

Apologetic 0.0324 0.0381 0.0872 0.0272 0.0378

(0.1773) (0.1919) (0.2827) (0.1631) (0.1824)

Destructive 0.1556 0.1458 0.1418 0.1303 0.1615

(0.3627) (0.3535) (0.3494) (0.3371) (0.3638)

Perfect 0.0365 0.0416 0.0254 0.0.0424 0.0274

(0.1877) (0.2001) (0.1577) (0.2018) (0.1737)

High education 0.2922 0.2986 0.2909 0.3272^* 0.3745^*

(0.4551) (0.4584) (0.4550) (0.4699) (0.4848)

Full-time employment 0.8457 0.7951^* 0.8654 0.8515 0.9003^***

(0.3614) (0.4043) (0.3418) (0.3561) (0.3000)

Two parents 0.7090 0.7465^* 0.6945 0.7636^*** 0.7113

(0.4545) (0.4357) (0.4614) (0.4254) (0.4539)

No siblings 0.1177 0.1423^* 0.1163^* 0.1363 0.0927^*

(0.3225) (0.3500) (0.3212) (0.3436) (0.2906)

One sibling 0.5101 0.4930 0.4727 0.5242 0.5670^

(0.5002) (0.5008) (0.5001) (0.5001) (0.4963)

First-born 0.5358 0.5277 0.5418 0.5393 0.5051

(0.4990) (0.5000) (0.4991) (0.4992) (0.5008)

Female 0.6671 0.6527 0.5854^* 0.7303^* 0.5051^*

(0.4715) (0.4769) (0.4935) (0.4444) (0.5008)

Income 0.6874 0.6111^*** 0.7236^* 0.6363^*** 0.7250^*

(0.4638) (0.4883) (0.4480) (0.4817) (0.4472)

n (%) 739 (100.0) 288 (39.0) 275 (37.2) 330 (44.7) 291(39.4)

	All n (%)	Smoke n (%)	Binge drink n (%)	Diet n (%)	Active n (%)
Self-secure	0.0690	0.0659	0.0872	0.0272^***	0.0343^**
	(0.2536)	(0.2486)	(0.2827)	(0.1631)	(0.1910)
Apologetic	0.0324	0.0381	0.0872	0.0272	0.0378
	(0.1773)	(0.1919)	(0.2827)	(0.1631)	(0.1824)
Destructive	0.1556	0.1458	0.1418	0.1303	0.1615
	(0.3627)	(0.3535)	(0.3494)	(0.3371)	(0.3638)
Perfect	0.0365	0.0416	0.0254	0.0.0424	0.0274
	(0.1877)	(0.2001)	(0.1577)	(0.2018)	(0.1737)
High education	0.2922	0.2986	0.2909	0.3272^*	0.3745^***
	(0.4551)	(0.4584)	(0.4550)	(0.4699)	(0.4848)
Full-time employment	0.8457	0.7951^***	0.8654	0.8515	0.9003^***
	(0.3614)	(0.4043)	(0.3418)	(0.3561)	(0.3000)
Two parents	0.7090	0.7465^*	0.6945	0.7636^***	0.7113
	(0.4545)	(0.4357)	(0.4614)	(0.4254)	(0.4539)
No siblings	0.1177	0.1423^*	0.1163^*	0.1363	0.0927^*
	(0.3225)	(0.3500)	(0.3212)	(0.3436)	(0.2906)
One sibling	0.5101	0.4930	0.4727	0.5242	0.5670^**
	(0.5002)	(0.5008)	(0.5001)	(0.5001)	(0.4963)
First-born	0.5358	0.5277	0.5418	0.5393	0.5051
	(0.4990)	(0.5000)	(0.4991)	(0.4992)	(0.5008)
Female	0.6671	0.6527	0.5854^***	0.7303^***	0.5051^***
	(0.4715)	(0.4769)	(0.4935)	(0.4444)	(0.5008)
Income	0.6874	0.6111^***	0.7236^*	0.6363^***	0.7250^*
	(0.4638)	(0.4883)	(0.4480)	(0.4817)	(0.4472)
n (%)	739 (100.0)	288 (39.0)	275 (37.2)	330 (44.7)	291(39.4)

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

Considering the background characteristics, students who smoke were more disadvantaged. We only consider the result that were statistically significant. Namely, the parents of these students, when compared with the full sample, were less likely to be engaged in full-time employment (79.51%versus 84.57%) and less likely to a high income (61.11%versus 68.74%). The binge drinking students were mainly male (41.46%versus 33.24%in the full sample). The dieting students mainly came from a dual family since 76.36%lived with both parents versus 70.90%in the full sample. In addition, these students were mainly females (73.03%versus 66.71%in the full sample) and did not come from a high-income family (63.63%versus 68.74%in the full sample). Finally, the physically active students mainly came from advantaged families, with highly educated-mothers (37.45%versus 29.22%in the full sample) and full-time working patents (90.03%versus 84.57%in the full sample) and were mainly male (49.49%compared with 46.42%in the full sample).

Table 3 reveals the extent to which different student types affect risk behavior. The regressions were run with and without control variables. We only consider the result that were statistically significant. Thus, according to the results, if the student was categorised as “self-secure” the likelihood of being on a diet was reduced by 29.00 percentage points (22.34 percentage points if the control was included). Similarly, the likelihood of being physically active was reduced by 21.23 percentage points (17.79 percentage points if the control were included).

Table 3

Relationship between four different student types and risk behavior (n = 739)

	Smoke		Binge drink		Diet		Active
Self-secure	–0.0184	–0.0053	0.0638	0.0462	–0.2900^***	–0.2234^***	–0.2123^***	–0.1779^***
	(0.0708)	(0.0720)	(0.1006)	(0.0716)	(0.0714)	(0.0723)	(0.0705)	(0.0711)
Apologetic	0.0709	0.0638	–0.0053	0.0302	–0.0739	–0.1131	0.0667	0.0448
	(0.1013)	(0.1006)	(0.1004)	(0.1002)	(0.1032)	(0.1017)	(0.1014)	(0.1003)
Destructive	–0.0290	–0.0367	0.0315	–0.0449	–0.0860^*	–0.0827^*	0.0176	0.0240
	(0.0495)	(0.0492)	(0.0417)	(0.0490)	(0.0504)	(0.0497)	(0.0496)	(0.0489)
Perfect	0.0568	0.0650	–0.0576	–0.0704	0.0746	0.0239	–0.1011	–0.1689^*
	(0.0857)	(0.0956)	(0.0421)	(0.0952)	(0.0975)	(0.0967)	(0.0958)	(0.0947)
Control		Yes		Yes		Yes		Yes

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

To explore the heterogeneous impact, a sub-group analysis was performed in Table 4 5, capturing the impact for, respectively, women and men, and for students from high- and low-income families. We only consider the result that were statistically significant. When the regressions were rerun for gender (Table 4), it was possible to observe that being a “destructive” female student reduces the likelihood of being on a diet by 17.47 percentage points, and being a “perfect” student reduces the likelihood of being physical active by 1.91 percentage points. For males, being “self-secure” was seen to reduce the likelihood of being on a diet by 29.68 percentage points and being “destructive” reduced the likelihood of binge drinking by 16.37 percentage points.

Table 4

Relationship between four different student types and risk behavior by gender

	Smoke	Binge drink	Diet	Active
Girls (n = 493)
Self-secure	0.1082 (0.1130)	–0.0363 (0.1119)	–0.1472 (0.1158)	–0.2061^* (0.1169)
Apologetic	0.0366 (0.1117)	0.0538 (0.1104)	–0.1995^* (0.1141)	–0.0052 (0.1158)
Destructive	0.0171 (0.0623)	0.0411 (0.0616)	–0.1747^*** (0.0634)	0.0460 (0.0646)
Perfect	0.0870 (0.0964)	–0.0844 (0.0954)	0.0150 (0.0989)	–0.0191^** (0.0997)
Boys (n = 246)
Self-secure	–0.0987 (0.0959)	0.0959 (0.0971)	–0.2968^*** (0.0894)	–0.1576^* (0.0856)
Apologetic	0.1782 (0.2266)	–0.0354 (0.2297)	0.2253 (0.2154)	0.2939 (0.2027)
Destructive	–0.1197 (0.0820)	–0.1637^** (0.0827)	0.0758 (0.0782)	–0.0001 (0.0739)
Perfect	–0.4911 (0.5068)	0.5824 (0.5128)	0.5010 (0.4822)	0.5640 (0.4541)

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

Table 5

Relationship between four different student types and risk behavior by income

	Smoke	Binge drink	Diet	Active
High income (n = 508)
Self-secure	–0.1157 (0.0830)	0.0686 (0.0847)	–0.2348^*** (0.0845)	–0.1466^* (0.0840)
Apologetic	0.0444 (0.1149)	0.0290 (0.1171)	–0.1286 (0.1175)	0.0666 (0.1164)
Destructive	–0.0080 (0.0587)	–0.0500 (0.0598)	–0.0546 (0.0600)	0.0441 (0.0594)
Perfect	–0.0186 (0.1131)	–0.0618 (0.1152)	0.0785 (0.1157)	–0.1762 (0.1143)
Low income (n = 231)
Self-secure	0.3094^** (0.1420)	–0.0208 (0.1378)	–0.2177 (0.1398)	–0.13026^** (0.1342)
Apologetic	0.0194 (0.2065)	–0.0002 (0.1983)	–0.0943 (0.2022)	0.0208 (0.1954)
Destructive	–0.0914 (0.2065)	–0.0569 (0.0870)	–0.1544^* (0.0883)	–0.0472 (0.0858)
Perfect	0.2405 (0.1812)	–0.0370 (0.1746)	–0.1195 (0.1780)	–0.0477 (0.1720)

^*p < 0.10, ^**p < 0.05, ^***p < 0.01.

After rerunning the regressions for, respectively, students from high- and low-income families (Table 5), it was found that for students from high-income families, being “self-secure” reduced the likelihood of being on a diet by 23.48 percentage points. For students from low-income families, being “self-secure” increased the likelihood of smoking by 30.94 percentage points and reduced the likelihood of being physically active by 13.02 percentage points.

4 Discussion

Our paper shows that different student types engage in different risk behaviors. It likewise shows that the link between the students’ type (see section Methods) and risk behavior varies according to class and gender. Class and gender has been widely documented in other researches. In a study of Italian university students, Zaccagni and their collaborators, [16] shown that females have a higher weight status perception than males. In the same vein, other researchers [5] found that students with more advantaged parents tended to have a better weight status perception than that of students with a disadvantaged parental background [5, 17].

An interesting finding of this study is that smoking students are more likely to more disadvantaged life conditions, since less proportion of their parents in full-time employment and also have a less high income. This evidence could have an impact in self-perception of body. To illustrate this point, we can see that the literature has shown that self-perception of body image depends on several determinants, such as psychological factors, as well as socio-cultural influences, like family, peers and ethnicity [18, 19]. Likewise, other researchers have shown that females and students with an advantaged parental background tend to be more dissatisfied with their bodies than males [20].

This study add to the literature [21] by showing how that there are considerable gender and class differences when it comes to the perception of self-body image as compared with actual body weight. This is a problem because body dissatisfaction can lead to eating disorders [22] and reduced mental health status [23]. Thus, our results raise concerns about the welfare of the college student and its impact on the entire population. All the more so because the young college students are the future workers and protagonists of the world we live. Increasing awareness of these outcomes should guide support for universities in the development of health prevention and promotion programs to ensure healthy and productive citizens in the future. Therefore, this study corroborates [24] and reinforces the importance to assess and develop strategies that reduce body dissatisfaction in academia. We suggest physical activity programs and interventions because are two strategies that could positively influence mental health but also reduce the risk of lifestyle-related diseases. However, it should be kept in mind that overemphasising physical activity can also lead to detrimental effects since individuals that are body-dissatisfied might find that the ideal and norms introduced as part of the program are unattainable. Dieting is another common strategy to counteract body dissatisfaction. However, with the extensive focus on dieting, one needs to be aware of the risk of eating disorders, which are most common among females of more advantaged families [25]. On the other hand, in obese dietary measures do not always work because a very recent study shows that there is a stigma associated with obesity that impedes the promotion of weight loss [26]. One way of achieving this is via monitoring and self-monitoring programs that can help raise problems awareness, as it is proposed and showed in scientific evidence [27].

On the basis of an university students-centred approach assessed in this study we can emphasise, in a policy perspective, that students’ well-being should be of interest for most universities since disengaged students, delaying studying and dropping out of the educational program are extensive costs. In this respect, it is important to pay attention to how different types of students observed in our study engage in risky behavior in order to draw prevention approaches for risk behavior rather than to take the help of cure. Cure is not a better way to remain away from any problem. This paper suggests that there is a need to strengthen the role of teaching health topics at university. Moreover, a training package is needed for university teachers, as well as tools for use in higher education, and policy recommendations for decision-makers and educational planners. Further research should attempt to consider the impact of factors other than pure academic achievement from a cost–benefit perspective. Easton and collaborators [28] recently studied the relationship among real, self-perceived and desired BMI in a large sample of Mexican adults, and suggested regular screening practices to counteract body image misperceptions and as a motivational factor for interventions.

The university has an important role to play in not only educating about health but also in promoting a broad understanding of body image and body dissatisfaction [8, 9]. Therefore, the most effective interventions in order to maintain good mental health are those suggested by other researchers [29] dealing with body image.

4.1 Limitations of this study

Although we contribute to the existing research by considering the dynamic interplay between body image and body weight, our use of a confounding variable approach can be subject to the endogeneity problem. For example, we could consider that it is student risk behavior that affects their body image. Further research should look for exogenous variation in students’ body image in order to address the endogeneity problem. One option could be to use a social experiment, providing students with a more realistic body image.

Another major critic is that our sample was collected from higher education facilities, in which the COST Action members have access to students in different educational programs. Applying such a sample selection method is beneficial to creating a high response rate.

Conversely, it skews the data since some fields of study are more represented in some countries than others, and so it is difficult to take advantage of the cross-country dimension using multiple regressions.

Footnotes

Acknowledgments

The dataset has been collected with the support and assistance of Thomas Nilsen (Norwegian), Marina Kuzman (Croatia) and Sigrid Michel (Germany) within the framework of the COST Action 1210 Appearance Matters.

Conflict of interest

None declared.

Funding

This work was supported by National Funds through the Fundação para a Ciência e a Tecnologia (FCT) within the Centre for Health Technology and Services Research (CINTESIS) R&D Unit (grant number UIDP/4255/2020).

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