Introduction

Food avoidance may be warranted or unwarranted. In some cases, avoiding specific foods may be necessary as part of a management plan for chronic medical illnesses such as hypertension [1], diabetes mellitus [2] or coeliac disease [3]. In these cases, healthcare providers work closely with their patients to make necessary alterations to their nutritional intake so as to optimize health outcomes. Alternatively persons may engage in food avoidance for psychological reasons [4]. This is often blamed on misguided and misinformed impressions of food content resulting in self-imposed restrictions. Food avoidance that is driven by psychological reasons is similar to eating pathologies such as anorexia nervosa, bulimia nervosa and binge eating disorder, where behavioural extremes are observed, such as dietary restrictions, binge eating, preoccupation with weight and shape along with excessive exercising and purging [5]. Food avoidance occurs when the quantity, frequency and variety of foods are restricted because of beliefs that certain foods are “unsafe” and should be “feared”. These ‘forbidden’ foods when consumed often result in increased feelings of guilt and anxiety [5].

More recently, there is a general heightened awareness of food content and the nutritional value of foods, even so with the development of an unhealthily excessive desire to ‘eat clean’, extending to the coining of a new term—orthorexia nervosa (ON), where there is an obsession with ‘healthy’ food and ‘proper’ nutrition [6, 7]. ON is associated with ‘healthier’ food selection such as whole wheat, gluten free goods, more sports activities and reduced alcohol consumption [6].

There are other unhealthy dietary behaviours and attitudes, which are of clinical significance, as they may be the harbinger of a formal eating disorder. These include: food restraint, excessive exercise, the use of laxatives and diuretics. These practises are often observed during college years and may be paradoxically associated in the medium to long-term with health conditions such as overweight/obesity and its concomitant risk of non-communicable diseases (NCDs), as well as the development of eating disorders [8, 9] and other mental illness (e.g., depression and anxiety) [10, 11]. These unhealthy behavioural choices and by extension unhealthy outcomes, may in part be due to the desire to be thin, lean, or muscular [12]. The main dietary resources used to inform these body ideals and nutritional choices are most often from non-experts abounding in the media, inclusive of diet fads and their unattainable promises [13]. Generally these diets encourage significant restriction or overuse of certain food groups [13–15,14,] which contradicts widely accepted expert nutritional recommendations, that advise that daily diets should include a combination of macronutrients, such as carbohydrates, fats, and protein, and micronutrients such as calcium, potassium, zinc and vitamin B12, which can be found in food sources such as milk, eggs, cheese, potatoes, etc. Nutrition experts strongly recommend against any significant alterations to this combination [8, 16].

Non-communicable diseases (NCDs) are a major public health concern [17], particularly in low- and middle-income countries [18] and the leading contributors towards NCD prevalence are poor diet and physical inactivity resulting in the high rates of obesity faced by many low- and middle-income countries (LMICs) [18].

In developed countries, the prevalence of obesity has been linked to several factors, including intense preoccupation with food intake, weight control, recurrent dieting, rigidity about intake [19], breakfast skipping, meat and egg intake for breakfast along with high intake of these foods throughout the day whilst avoiding carbohydrates [20, 21], as well as insufficient overall food intake and insufficient exercise [22]. Diets poor in macronutrients have also been associated with anxiety about eating [23, 24], body dissatisfaction [25], mood and anxiety disorders [10, 11] and even eating disorders such as anorexia nervosa, bulimia nervosa or binge eating disorders [16]. Disordered eating behaviours, inclusive of food avoidance, may be associated with diets poor in macronutrients, excessive consumption of vegetables and inadequate amounts of cereals, grains, and meat [29], excessive exercising [26, 27], and even binge drinking [26,27,28,29,30,31,32].

There has been growing research on the high prevalence of obesity [33], disordered eating behaviours [34,35,36], and binge drinking [37] among college students in developing countries, and although several risk factors have been identified such as the media, globalization and body dissatisfaction, there are no known studies that have explored the influence of food avoidance. The main objective of this study is to determine whether food avoidance exists among college students in low- and middle-income countries and to explore its relationship with body mass index (BMI), dieting, mood/anxiety disorders, physical activity and general health knowledge. Our secondary objective is to determine if binge drinking was associated with food avoidance, dieting, high physical activities and PTSD and depressive symptoms.

Methods

Study population and procedure

This study was a subset of a larger cross-sectional survey conducted in 26 countries from Asia, Africa and the Americas. This survey focused on health behaviours among college students such as lifestyle practises such as diet, exercise, sexual behaviours, depression, PTSD and general health knowledge. Convenient sampling technique was used with the sample size calculated at 800 university/college students aged 18–30 years for each participating country, with equal gender representation (400 men and 400 women).

Universities involved in this study were located in the capital city or another major city in the participating countries and participants were selected from undergraduate classes using a quasi-random selection process. One department was randomly selected from each University faculty, and a random selection was then made from an ordered list of all undergraduate courses offered within the selected department. Trained research assistants explained the study to students within the selected undergraduate class to recruit participants. After receiving voluntary written consent, the study questionnaire was then self-administered to participants. Inclusion criterion was being presented in class at the time of recruitment.

A total of twenty-one thousand and seven (21,007) participants from 26-countries (see Table 1) completed the larger study with country participation rate of over 90% for most countries, with few participating locations meeting only 50% of their recruitment target. The students who completed the survey varied in the number of years for which they had attended the university (range = 1–4 years). Participants were recruited from varied majors including education, humanities and arts, social sciences, business and law, science, engineering, manufacturing and construction, agriculture, health and welfare and services. Further details of the study protocol, including demographic variables have been previously described [36]. This current study is limited to participants that reported the eliminated foods from their diet (n = 6096, 28.6%) (see Table 1).

Table 1 Participating countries in survey
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Data were collected from September 2013 to May 2014. No incentive was included for participation, and there were no penalties for refusing to participate.

The study was approved by the local ethics committees of each participating site and conforms to the ethical principles of the Declaration of Helsinki.

Measurements

The questionnaire used for data collection was developed in English, was translated and then back-translated by local experts into several target languages for each participating country where the first language was not English.

Socio-demographic data recorded included age, sex, marital status, residential status and self-perceived socioeconomic status by rating their family background wealth as ‘wealthy’ (within the highest 25% in country), ‘quite well off’ (within the 50–75% range for their country), ‘not very well off’ (within the 25–50% range for their country), or ‘quite poor’ (within the lowest 25% in their country) [38].

Dietary behaviours

Items were selected from the Health and Behaviour Survey [developed by 39], the “national college health risk behavior survey”- to ascertain dietary behaviours. Dietary behaviours assessed included: (a) frequency of consumption of red meat; (b) frequency of consumption of fruit; (c) addition of salt to food (usually, sometimes, occasionally, never); (d) trying to avoid fat and cholesterol (yes, no); and (e) trying to eat fibre (yes, no) [40]. The fibre and fat items were each followed by an open-ended question asking what foods the individual either avoided or ate. The responses were then coded into food categories of those foods avoided—carbohydrates, fats and protein. Perceived body size (ranging from ‘very fat’ to ‘very thin’) were also rated by each participant, and current weight loss practises were explored using two items: “Are you trying to lose weight?” and “Are you on a diet?”

Binge drinking (alcohol) was measured by asking participants, “how often do you have five or more (for men) and four or more drinks (for women) on one occasion?” Response options were 0 = never, 1 = less than monthly, 2 = monthly, 3 = weekly and 4 = daily or almost daily. For the purposes of this study, participants were assigned as ‘Binge drinking’ if they responded as at least monthly [41].

Physical activity was assessed using the International Physical Activity Questionnaire short version (IPAQ-S7S) [42]. The IPAC is a self-report measure of the typical number of minutes spent each week (1) in walking (assuming 3.3 metabolic equivalents (Mets) per minute); (2) in moderate activity (e.g., carrying light loads or regular bicycling) (4 Mets per minute); and (3) in vigorous activity (e.g., fast bicycling, aerobics, etc.) (8 Mets per minute) (20).

Depression

The Center for Epidemiologic Studies Depression, CES-D-10 is the shortened version of the 20-item CES-D questionnaire developed by Andersen et al. [43]. It is a 10-item instrument assessing depressed mood over the previous week utilizing a Likert scale, ranging from ‘rarely’ (less than 1 day) to ‘most or all of the time’ (5–7 days). Scores on this measure range from 0 to 30 with a score of 10 or more indicating the presence of depressive symptoms. Both the 20-item and 10-item CES-D show excellent capacity to identify individuals with depression [44], with the CES-D-10 demonstrating substantial predictive accuracy when compared to the full-length 20-item version of the CES-D (κ = 0.97, p < 0.001) [43]. The Cronbach’s alpha score for the current study was 0.76.

Short screening scale for DSM-IV post-traumatic stress disorder (PTSD)

This seven-item scale is assessed for post-traumatic stress disorder symptoms. Participants were asked to indicate ‘yes’ or ‘no’ to questions—five of which explored symptoms of avoidance and numbing while the other two assessed for hyperarousal symptoms. A score of 4 or greater on this scale is used to define positive cases of PTSD with a sensitivity of 80%, specificity of 97%, positive predictive value of 71%, and negative predictive value of 98% [45]. Cronbach’s alpha for this study was 0.78. The short screening scale for post-traumatic stress disorder used was based on the DSM-IV criteria in view of the study protocol having been established and approved prior to the release of the DSM-V in May 2013. However, the four main clusters of PTSD in the DSM-IV are the same as that of the DSM-V and so no significant variation is expected when assessing for clinical significance.

Risk awareness or health knowledge

The risk awareness items included evaluating the knowledge or lack thereof (yes/no) of how health behaviours contributed to health problems; the health areas explored were heart disease, high blood pressure, lung cancer, breast cancer and smoking [38].

Anthropometric assessment

Trained personnel measured weight and height using standard techniques in all participating countries except in China and Indonesia, where participants self-reported their weight and height. The anthropometric measure of excess adiposity used as outcomes were: overweight—body mass index (BMI) 25–<30 kg/m2 and obesity—BMI ≥30 kg/m2.

Results

Sample description

More than a half of the sample for the current study were women (n = 3644, 57.6%), self-reported as being ‘wealthy’ (n = 2832, 52.9%), and were from low income countries (n = 3482, 57.1%). The vast majority of participants reported that they were in good health (n = 5554, 91.6%). The majority of participants were of normal weight (n = 3340, 62.7%), and approximately one in five was either underweight (BMI <18.5%) (n = 866, 16.2%) or overweight/obese (BMI ≥25) (n = 854, 15%) category. The most frequently reported food category avoided was fats (n = 4265, 70%), followed by proteins (n = 1142, 19%), and carbohydrates (n = 426, 7%), with just over one-third of the participants (n = 2143, 35.8%) reporting they were trying to lose weight and 16% (n = 942) were currently dieting. Just over 10% of participants (n = 757) reported significant depressive symptoms (CES-D-10 score >10), while one-fifth of participants (20.1%) had significant post-traumatic stress disorder (PTSD) symptoms (score ≥4) see Table 2.

Table 2 Sample description
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Chi-square analyses were conducted to assess the relationship between predictor variables and food categories eliminated from participants’ diet (Table 3). A significant relationship was found between persons who were trying to lose weight and the avoidance of fat (χ 2 (1) = 5.33, p < 0.05), protein (χ 2 (1) = 4.23, p < 0.05) and carbohydrates (χ 2 (1) = 6.89, p < 0.05). A significant difference was also found where persons who reported they were dieting to lose weight were more likely to avoid protein (χ 2 (1) = 5.07, p < 0.05) and carbohydrates (χ 2 (1) = 5.52, p < 0.05). There were significant differences noted by country’s economic ranking with low and lower middle-income countries more likely to report avoiding fat (χ 2 (1) = 340.85, p < 0.05) and protein (χ 2 (1) = 75.84, p < 0.001), and upper middle-income countries more likely to avoid carbohydrates (χ 2 (1) = 15.13, p < 0.05). Participants who engaged in high intensity physical activities were also significantly more likely to avoid fat (χ 2 (1) = 15.27, p < 0.001), protein (χ 2 (1) = 7.79, p < 0.05) and carbohydrates (χ 2 (1) = 5.81, p < 0.05). Participants who perceived themselves as not wealthy (‘not well off’ or ‘poor’) were more likely to avoid protein (χ 2 (1) = 5.18, p < 0.001) see Table 3.

Table 3 Food categories eliminated from diet by socio-demographic and psychosocial factors
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Depressive symptoms, PTSD and food avoidance

Also indicated in Table 3, persons who suffered from significant depressive symptoms (CES-D-10 score >10) were not more likely to eliminate carbohydrates from their diet (p > 0.05). One-fifth of participants who reported PTSD symptoms within the clinical range (score ≥4), had similar prevalence of avoidance of major food categories—carbohydrates (20.7%), fats (20.4%) and protein (19.5%) with those without PTSD (p > 0.05).

Weight loss and dieting

As indicated in Table 3, a statistically significant relationship was found where more than the majority of participants who were trying to lose weight reported that they avoided protein (66.9%, p = 0.04), fats (62.7%, p = 0.021) and carbohydrates (58.3%, p = 0.009). The results indicate a significant relationship between dieting to lose weight and food avoidance. The majority of the sample who were dieting avoided protein (86.3%, p = 0.073) and one-fifth avoided carbohydrates (79.9%, p = 0.019).

Food avoidance by country, physical activities and health knowledge

A significant difference was found between lower middle-income countries and upper middle-income countries with regards to fat avoidance. The majority of participants who avoided fat were from the lower middle-income countries (69.9%, p < 0.001). As for protein (54.4%, p < 0.01) and carbohydrates (51.8%, p < 0.01), more than a half of the participants were from upper middle-income countries (Table 3). The results indicate statistically significant relationships among the following: the majority of persons who engage in high activity exercises were more likely to avoid fats (60.2%, p < 0.01), protein (53.8%, p < 0.01) and carbohydrates (51.9%, p < 0.05, Table 3).

Food avoidance and health knowledge

As seen in Table 4, fat avoidance and health knowledge yielded several significant findings whereby, the majority of participants were knowledgeable about the association between fat and heart disease (73.5%, p < 0.01), heart disease and alcohol (70.5%, p < 0.01) being overweight and high blood pressure (64.4%, p < 0.05), and heart disease and exercise (59.8%, p < 0.05). Less than one in 5 participants were aware of the association between fat and breast cancer (15.5%, p < 0.001). Less participants who avoided protein were knowledgeable about the connection between smoking and heart disease (44.7%, p < 0.05) and fat and breast cancer (10.8%, p < 0.05). The majority was knowledgeable about fat and heart disease (64.9%, p < 0.01) and heart disease and alcohol (62.8%, p < 0.05). Lastly approximately one half of the sample who avoided protein was knowledgeable about heart disease and exercise (50.4%, p > 0.05, Table 5). A significant relationship showing that only 18.4% of persons who avoid carbohydrates were knowledgeable about the connection between fat and breast cancer (p < 0.01, Table 6).

Table 4 Relationship between avoiding fat foods and knowledge of health conditions and associations
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Table 5 Relationship between avoiding protein foods and health knowledge conditions
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Table 6 Relationship between avoiding carbohydrate foods and health knowledge conditions
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Multivariate logistic regression analyses examined risk factors for binge drinking, high physical activity, being underweight, exhibiting significant symptoms of PTSD and depressive symptoms. The exponential B weights (eB) provided in Table 7 represents the odds or likelihood of an event occurring. The results from the logistic regressions showed that majority of the variables included in the model were statistically significant predictors of binge drinking. Individuals living in upper middle-income countries are three and a half times more likely to report binge drinking (OR = 3.60, 95% CI = 3.25–3.99, p < 0.05) compared to individuals in lower income countries; however, those self-reporting as ‘wealthy’ were less likely to binge drink (OR = 0.89, 95% CI 0.81–0.98, p < 0.05). Men were twice as likely as women to report binge drinking (OR = 2.35, 95% CI = 2.26–2.69, p < 0.05). We also found that individuals who reported dieting to lose weight were 30% more likely to binge drink compared to those who did not diet to lose weight (OR = 1.30, 95% CI = 1.147–1.473, p < 0.05).

Table 7 Summary of logistic regression analysis for binge drinking
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The results also revealed that individuals who live away from their parents were 16% more likely to report binge drinking compared to those who live with parents (OR = 1.16, 95% CI = 1.056–1.272, p < 0.05), while married individuals were 56% more likely to report binge drinking than their unmarried peers. Results also indicate that persons who binge drink were 24% more likely to engage in high physical activities (OR = 1.24, 95% CI = 1.12–1.36, p < 0.05) and were 0.89 times less likely to be from a wealthy background (OR = 0.89 95% CI 0.81–0.98, p < 0.05).

Half of the predictors were statistically effective in predicting high physical activity (Table 8). Males were two and a quarter times more likely to engage in high physical activity than females (OR = 2.25, 95% CI −2.11 to 2.41, p < 0.05). Additionally, people who engage in high physical activity were more likely to diet to lose weight (OR = 1.25, 95% CI = 1.15–1.37, p < 0.05). The results also indicated that persons who engage in high physical activities were more likely to be married (OR = 1.39, 95% CI = 1.17–1.64, p < 0.05) and labelled themselves as being wealthy (OR = 1.23, 95% CI = 1.15–1.31, p < 0.05).

Table 8 Summary of logistic regression analysis for high physical activities
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Four of the eight predictors were effective in predicting participants who were underweight X 2 (3) = 4.67, p < 0.05. Our results indicate that the odds of being underweight is 37% less likely in individuals who avoided fat compared to those who did not avoid fat (OR = 0.63, 95% CI = 0.57–0.69, p < 0.05) and 46% less likely in males than females (OR = 0.54 95% CI = 0.49–0.59, p < 0.05). We also found that the odds of being underweight is 20% less likely in individuals from lower income countries compared to upper middle-income economic countries (OR = 0.80, 95% CI = 0.73–0.87, p < 0.05) and 34% less likely in individuals 22 years or older compared to those who were 21 and younger (OR = 0.66, 95% CI = 0.59–0.74, p < 0.05).

Further logistic regression analyses found three variables predicted depression in participants. The results revealed that individuals who reported severely depressed symptoms were one and one-fifth times more likely to diet to lose weight (OR = 1.21, 95% CI = 1.08–1.36, p < 0.05). The odds of being depressed is 8% less likely from an upper middle-income country than from a lower income (OR = 0.92, 95% CI = 0.84–0.10, p < 0.05) and 14% less likely in those living away from parents than living with parents (OR = 0.86, 95% CI = 0.79–0.94, p < 0.05) (see Table 9).

Table 9 Summary of logistic regression analysis for depressed
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Dieting to lose weight, income level of the country and self-reported wealth were effective in predicting PTSD. The results indicate that persons with PTSD symptoms were one and half times more likely to engage in dieting behaviours to lose weight (OR = 1.49, 95% CI = 1.35–1.64, p < 0.05). Results also reveal that the odds of having PTSD was 17% less likely in persons who reported to be wealthy than those who were not (OR = 0.83, 95% CI = 0.83–0.97, p < 0.05). Results also indicated that the odds of having PTSD is also 17% less likely in persons from the upper middle-income class than from the lower income class (OR = 0.83, 95% CI = 0.77–0.90, p < 0.001) (see Table 10).

Table 10 Summary of logistic regression analysis for exhibiting symptoms of PTSD
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In assessing the impact of food avoidance on BMI, the multivariable linear regression analysis revealed the results in Table 11. There was no significant relationship between BMI and avoidance of protein. Avoidance of fats significantly predicted BMI scores that is, the more individuals avoid fat, the higher the BMI (OR = 0.27, 95% CI = 0.974–0.1.027, p < 0.05) (see Table 11).

Table 11 Multiple regression results of food avoidance predicting BMI
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Discussion

This study explored the prevalence of food avoidance among university students across 26 lower–middle and upper–middle income countries. We observed that food avoidance (of fats, carbohydrates and protein) exists in as many as one-third of university students in lower and upper middle-income countries, with this being more likely in persons who are trying to lose weight whether by dieting or otherwise and in those with higher BMI. Interestingly, lower middle-income and upper middle-income countries differed with respect to the foods they avoided. The presence of depressive symptoms, high intensity exercise as well as the level of health knowledge also influences the types of food avoided. Some of the findings of this study are consistent with previous studies, while others add new theoretical and clinical understanding about food avoidance and the role it may play in dieting behaviours, high intensity exercises, PTSD and depressive symptoms and high BMI.

The high prevalence of food avoidance identified, involving 1 in 3 university students in lower and upper–middle income countries holds clinical and public health utility for the way we treat and address the growing prevalence of eating pathologies and obesity in developing countries [46,47,48,49].

Those participants who engaged in food avoidance had significantly better health knowledge surrounding the foods avoided and the bona fide associations with health risks—fat and protein avoiders were aware of the relationship between fat and heart disease, heart disease and alcohol, and heart disease and exercise; however, only persons who avoided fats were knowledgeable about associations between overweight and high blood pressure. Indeed, the presence of a higher BMI among those avoiding fats in our study, may be as a result of their requirement to attempt weight loss to avoid medical complications related to fat intake. However, we wish to postulate other explanations for food avoidance in our study population.

Food restraint may be health-related if diagnosed with a chronic condition such as hypertension, diabetes and obesity [8,9,10]; however, when not diagnosed with these types of conditions, food restraint can be observed as typical of chronic dieting patterns, driven by a desire for weight loss [26] and not necessarily for health-related reasons [5]. Further, individuals who have clinically significant eating and weight concerns, are likely to also exhibit chronic dieting, excessive exercise, weight gain or excessive vegetable intake [22]. Food avoiders in our study demonstrated similar patterns to these clinically significant eating and weight concerns group, as they were more likely to engage in high intensity exercise, diet or be trying to lose weight and have a higher BMI. Additionally we also found that persons who were underweight were less likely to avoid fats (Table 12). These findings may, therefore, be in support of previous studies elsewhere, showing that restrained eating can be accompanied by binge episodes that can significantly alter individuals’ weight and thereby their BMI [26]. Further support for consideration of possible disordered eating behaviours and or eating disorders which may co-occur with other psychopathologies [50], are our findings of participants with more depressive symptoms and PTSD being more likely to engage in food avoidance.

Table 12 Summary of logistic regression analysis for being underweight
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Food avoidance is a deeply embedded thought, behaviour and emotion that serves to perpetuate eating pathologies. The thoughts associated with food avoidance are distorted with regard to the characteristics of food as well as to consuming the food [49], so much so that the individual engages in self-imposed restrictions. Perhaps what may be reinforcing this belief system is the burgeoning obsession with nutritional properties of foods and the practise of placing them in ‘cleaner’ and ‘safer’ food categories [6, 7]. Irrational beliefs about foods and what is considered to be healthy may lead to obsessive eating behaviours [51]. While our study did not ascertain the reasons behind the food avoidance, we did identify similar associations among those who avoided certain macronutrients as that noted in persons who engage in food categorization, even with orthorexia nervosa.

Perhaps the root of these nutrition choices can be linked to a person’s perception of food primarily as a source of weight gain, rather than its intended purpose, a source of energy and nourishment [7]. Carbohydrates and fats have been labelled as the main culprits for weight gain, while high protein is linked to weight loss [8, 9]. Carbohydrates and fats are seen as the “fear” or “forbidden” foods and are generally restricted or even fully eliminated from dietary intake [13,14,15]. Making the right food choice may be akin to peer pressure, Barnett et al. speak of a ‘moral’ and ‘ethical’ pressure to choose responsibly [7]. Restrictive diets; however, have attendant risks, which seemingly are not adequately considered as persons autonomously self-restrict in the absence of good evidence to do so [52]. Exclusively low carbohydrate and high protein diets have been associated with increased mortality, NCDs (cardiovascular diseases, cancer), obesity and early ageing [8, 9, 53].

In addition to the cognitive aspect, there is also the emotional aspect of food avoidance. The intensity of these emotions are said to be equivalent to any form of phobia and serves to further perpetuate the drive to engage in unhealthy weight compensatory behaviours. Unfortunately, these disordered eating habits and behaviours in the college environment are deemed as “normal” eating habits and if broken cause grave distress [5].

Of the few studies conducted on restrictive or dieting behaviours in developing countries, researchers are alarmed that their prevalence rates are comparable to developed countries [49]. In support of this literature, we also found that the participants in LMICs, like developed countries also avoided macronutrients such as fats and carbohydrates [20], but unlike their high-income counterparts, they also reported avoiding protein. One explanation for protein avoidance in this study could be an increased reliance on vegetables, which is typical of persons who engage in restrictive eating behaviours; restriction for religious reasons or avoidance secondary to financial limitations, as a little over a half of our participants who avoided protein, self-reported as being in the ‘not wealthy’ category. Further work should be done to explore the reasons for food avoidance in LMICs using qualitative assessment.

Another possible correlation of food avoidance is its association with mood or anxiety related symptoms. The findings in this study serve to support this as we found a significant association between food avoidance and depression. Persons who were depressed were more likely to eliminate carbohydrates from their diet and more likely to diet to lose weight. Studies show a positive relationship between affective disorders and disordered eating behaviours, where binge eating for instance is associated with changes in mood [10, 54]. Although we did not find a significant relationship between PTSD and food avoidance, our findings showed that persons with PTSD symptoms were 1.5 times more likely to diet to lose weight. Similarly, we found that severely depressed participants were 1.5 times more likely to diet to lose weight. The fact that these psychopathologies were related to dieting to lose weight is consistent with previous literature, which highlights that persons diagnosed with these conditions may try to ‘self- medicate’ by controlling their weight [5].

While we did not find a significant relationship between food avoidance and binge drinking, we did find that binge drinking was 30% more likely in persons dieting to lose weight. It is possible that this method of consuming alcohol may be due to several reasons as proposed by the literature, these include: wanting to get a greater effect of the alcohol, trying to reduce caloric intake by restricting before drinking, as well as being able to consume large amounts of alcohol [26,27,28,29,30,31,32].

Despite the advice available and accessible from nutritional experts, many adolescents and young adults seem misinformed, as they resort to the advice of non-experts for their dietary information [17]. The overreliance on non-medical or non-scientific information appears to be widespread, with a study revealing adults in Australia taking autonomous approach to their food choices by eliminating dairy from their diet [52]. The concern with these inappropriate nutrition choices is that by eliminating something beneficial from their diets they risk developing other negative health outcomes. While college students may engage in these unhealthy weight compensatory behaviours, the irony is that it is not unusual for them to appear knowledgeable about other health-related matters. For example, persons diagnosed with anorexia nervosa, despite their abnormal food intake are seen as more knowledgeable about the nutritional components of foods [16].

Studies have also shown that while persons with eating pathologies may be aware of some of the nutritional properties of food, much of the information they have is inaccurate [5]. Unfortunately, while the information is false, it is fixed in their minds as studies have shown that while college students may possess the requisites for critical thinking, they are more susceptible to diet fads [5, 10,11,12,13, 17, 20]. Notwithstanding this, there may be hope for a more positive outcome if provided with the correct information. In that, one study showed where college students who majored in nutrition were more knowledgeable about the nutritional properties of foods, this served as a protective mechanism against them developing orthorexia nervosa [51].

Lastly we found that countries’ economic categorization was significantly related to the types of foods avoided. The majority of participants who avoided fat were from the lower middle-income countries, whereas for protein and carbohydrates, more than a half of the participants were from upper–middle income countries. While affluence has been used to explain the existence of disordered eating behaviours, the fact that both categories of countries engage in food avoidance may be indicative of the college community environment itself contributing to a vulnerability to eating pathologies [8, 9].

Limitations

While our study set out to explore food avoidance, we were limited in not having other measures that could help to support the assumptions made, for example, screening questions for disordered eating behaviours and attitudes to determine risk of an eating disorder. Additionally, further exploration of other foods that participants avoid or over indulge in, such as vegetables, as well as other comorbidities associated with food avoidance such as binge drinking. While these may have added further explanations to our findings, we cannot negate that the associations found in this study are compelling. We recommend that further studies be conducted in developing countries utilizing an even more holistic study in the exploration of disordered eating behaviours and associated psychosocial, emotional, behavioural and socioeconomic factors including depression, anxiety and binge drinking. We also cannot ignore the role of the experts, therefore, in ruling out disordered eating behaviours future studies should explore whether the food avoidance was medically related and if the advice given was from a physician.

Future studies can also explore if the food avoidance was self imposed, what source informed the food avoidance (such as diet fads, religion, peer pressure) along with whether or not eating pathologies such as orthorexia played a role. Lastly, for countries where their first language is not English, the translation may pose a problem to the psychometric properties of the test, therefore, future studies should ensure that the questions are standardized to safeguard against translation error.

Conclusion

Although food avoidance may be considered a central driving force behind the development of eating pathologies, it has not received much attention, as most studies seem to focus mainly on the negative health outcomes of eating pathologies [53]. To our knowledge, our study is the first known of its kind that examined food avoidance in low- and middle-income countries. Like studies conducted in developed countries, we found an avoidance of macronutrients, which were also, associated negative health outcomes such as higher BMI and depressive symptoms. Despite being knowledgeable about health-related behaviours, we found that college students in our sample were not that different from those in developed countries and may be influenced by similar advice given by non-experts about macronutrients. These results hold implications for intervention programmes and policy makers. Future interventions should include a programme to educate college students on healthy eating lifestyle from expert sources, perhaps as a fundamental required course within the first year(s) of college. Future work should also explore the role of underlying psychopathologies such as PTSD and depression with interventions to treat these symptoms as they could possibly play a key role in the mitigation and even eradication of food avoidance behaviours.