research articles on fast food

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Original Article
• Open access
• Published: 20 July 2015

Health Implications of Adults’ Eating at and Living near Fast Food or Quick Service Restaurants

• A V Moudon 2 ,
• S Y Kim 3 ,
• P M Hurvitz 2 &
• A Drewnowski 4  

Nutrition & Diabetes volume  5 ,  page e171 ( 2015 ) Cite this article

10k Accesses

38 Citations

20 Altmetric

Metrics details

• Cardiovascular diseases
• Risk factors

Background:

This paper examined whether the reported health impacts of frequent eating at a fast food or quick service restaurant on health were related to having such a restaurant near home.

Logistic regressions estimated associations between frequent fast food or quick service restaurant use and health status, being overweight or obese, having a cardiovascular disease or diabetes, as binary health outcomes. In all, 2001 participants in the 2008–2009 Seattle Obesity Study survey were included in the analyses.

Results showed eating ⩾ 2 times a week at a fast food or quick service restaurant was associated with perceived poor health status, overweight and obese. However, living close to such restaurants was not related to negative health outcomes.

Conclusions:

Frequent eating at a fast food or quick service restaurant was associated with perceived poor health status and higher body mass index, but living close to such facilities was not.

Similar content being viewed by others

Trends in the healthiness of U.S. fast food meals, 2008–2017

Large-scale diet tracking data reveal disparate associations between food environment and diet

The retail food environment and its association with body mass index in mexico, introduction.

The rise of obesity has been explained by dietary changes and parallel increases in the supply and consumption of high-energy but low-nutrient foods. Over the past three decades, trends show increases in eating away from home and energy gained from sugars and fats. 1 , 2 In a longitudinal prospective study, out-of-home eating, including at fast food restaurants, was associated with weight status and multiple metabolic outcomes. 3 Eating at fast food restaurants was found to relate to poorer diets in the general population, 4 in a group of pregnant women, 5 and to higher rates of obesity in rural areas of the Midwest. 6 However, the health effects of eating out frequently might differ for men and women. 7

Poor diets and health may be due to increases in the number of fast food and other pay-before-you-eat establishments, which offer a restricted choice of healthy out-of-home foods. 8 Hence public health professionals have hypothesized that increased exposure to the now ubiquitous restaurants might be the mechanism through which health and diets might be impacted. In response, a large literature investigated how proximity and access to fast food restaurants relate to health. Examining associations between home-based distances to or density of restaurants near homes and a variety of health outcomes, this literature produced mixed results. A review of 40 articles found that access to fast food restaurants was related to higher body mass index (BMI) in six studies, and not related in four studies. 9 Another review found some evidence that residents with limited access to fast foods had healthier diets and lower levels of obesity. 10 The local food environment was associated with recommended dietary intake 11 and with the prevalence of obesity. 12 One study found that multiple food options in a neighborhood decreased the risk of being obese. 13 Obesity prevalence was linked to living in states or counties with higher density of fast food restaurants; 14 in census units with more fast food restaurants or convenience stores; 15 and in areas with higher concentrations of local restaurants. 16 No such associations were found in other studies. 17 , 18 , 19 One study found lower BMI and lower prevalence of obesity to be associated with living in an area with higher densities of stores offering a choice of healthy foods, but not with higher densities of restaurants. 20 Weight gain was higher in older adults living nearer fast food restaurants. 21 And while no association was found with cardiovascular mortality, 22 the risk of having a stroke was shown to increase with the density of fast food restaurants in the home neighborhood. 23

Disadvantaged groups also appeared more likely to live in neighborhoods with concentrations of unhealthy food sources than their wealthier counterparts. 24 , 25 Of 40 studies reviewed, 12 found fast food restaurants to be more prevalent in areas housing ethnic minority groups. 9 Another review of 33 studies found 14 studies where the availability of fast food outlets was correlated with higher deprivation; but 13 studies that yielded conflicting results. 26 In several North American studies, fast food accessibility was correlated with different measures of neighborhood deprivation, 27 , 28 and with the odds of being obese. 29 , 30 , 31 No such association was found in another study. 32 In contrast, low-income residents of rural areas were found to live closer to healthier fares offered in fast food outlets than their higher-income counterparts. 33

So far, most studies measured exposure as the spatial proximity to food in the home or school neighborhood environment. Few were able to relate to a population’s actual consumption pattern, including actual food intake, food purchases or the prevalence of eating out. 1 , 7 , 11 , 17 Even more importantly, they lacked data on where people actually ate out or purchased food, and specifically whether they ate or purchased food in the neighborhood of exposure. The lack of spatially matched data on exposure and consumption makes it difficult if not impossible to untangle the direct versus the indirect effects of exposure on behavior. One study found that access to outlets with take-away or fast food could not predict the consumption of foods offered in these establishments. 34 These limitations, plus the fact that most studies remained cross-sectional, and used different, typically untested measurements of environment, might in part explain the mixed results yielded to date.

This study uses self-reported data on eating at and using specific fast food and quick service restaurants. Geocoded participants’ home and work addresses and locations of all restaurants served to examine whether using fast food and quick service restaurants and living close to them were associated with reported health status, being overweight or obese, having cardiovascular disease (CVD) or diabetes.

Materials and methods

Participants came from the Seattle Obesity Study, a population-based study of social disparities, diet quality and health in King County, WA. A 20-min telephone survey was conducted on a stratified random sample of 2001 randomly selected adult residents of King County, WA. Administered between October 2008 and March 2009, the survey was approved by the Institutional Review Board at University of Washington. Seattle Obesity Study participants were representative of the population in King County in terms of race and ethnicity, income and household size; and similar to the Behavioral Risk Factor Surveillance System 2007 sample in terms of age and gender. The participants’ individual demographic and socioeconomic characteristics included age, gender, ethnicity, number of children <12 years, number of children between 12 and 18 years, household size, education, employment and annual household income ( Table 1 ).

Fast food and quick service restaurant use

The use of fast food or quick service restaurants came from the survey question ‘when you eat out, how often do you go to a quick service restaurant?’ where the respondent was told ‘a quick service restaurant is a place where you pay before you eat, such as a fast food restaurant or coffee shop.’ Respondents could select one of four answers: (1) never or ⩽ 2 times a month; (2) 2 to 3 times per month; (3) once a week; (4) 2–3 times per week; and (5) >4 times per week. Following accepted healthy eating standards, 35 the data were dichotomized at (4), 2–3 times per week, henceforth noted as <2 and ⩾ 2 a week. Respondents were then asked ‘which quick service restaurant do you go to most often,’ and prompted to provide the name of the restaurant. They also gave the names of the principal streets at the closest street intersection of the restaurant, which allowed researchers to locate the restaurant most frequently used relative to those in the County’s inventory.

Fast food and quick service restaurants

The restaurants were extracted from the food permit data provided by the Public Health Seattle and King County, which contained 10 254 records. Fast food restaurants were classified by the University of Washington Urban Form Lab as those of nationally recognized chains that sold meals that had been designed off-site, and were expected to be ready by the time a customer’s change is handed over. 36 , 37 Similar to fast food restaurants, quick service establishments did not offer full table service and sold meals that had been designed off-site. They promised a somewhat higher quality of food and atmosphere, but often at higher average prices than charged at fast food restaurants. 38 Each one of the 606 fast food and 2395 quick service restaurants in the sample had one food permit. Under the quick service category, there were 302 permits identifying bakeries/delis, 786 indicating that ethnic foods were offered, and 1307 offering standard food. Fast food and quick service restaurants constituted 46% of the total inventory of restaurants in King County. 39

Outcome variables

The health outcome, general health status, was from the survey question 'Would you say that in general your health is: excellent/very good/good/fair/poor?' The measure was dichotomized into perceived fair/poor health versus otherwise.

Survey questions on weight and height were used to calculate BMI. Participants with a BMI ranging between 25 and 29.99 were classified as being overweight, and those with a BMI of ⩾ 30 kg/m 2 were obese.

Having diabetes or CVD was from two survey questions ‘Have you ever been told by a doctor, nurse or another health professional that you have diabetes/any kind of heart disease?’ These health outcomes were dichotomized.

The residential and work addresses of Seattle Obesity Study respondents were geocoded to the centroid of the home or work parcel using the 2008 King County Assessor parcel data. Geocoding followed standard methods in ArcGIS, version 9.3.1 (ESRI, Redlands, CA, USA). Address records that failed the automatic geocoding (30% using a 100% match score) were manually matched using a digital map environment with annotated layers from the reference data augmented by online resources such as GoogleMaps, QwestDEX, and Yelp. Each home and work point was double-checked by a separate technician for plausibility (the parcel being a residential land use) and accuracy (the location being on the correct parcel).

Fast food and quick service restaurants included 3001 food permit records out of the Public Health Seattle and King County 10 254 records. Permit addresses were also geocoded to King County parcel centroids, and using ArcGIS, version 9.3.1 (ESRI); 99.6% of the food permit addresses were geocoded.

Distance to frequently used and closest restaurant

Distance measures were computed from each respondent’s home and work to the fast food and quick service restaurant that respondents reported using and to the same type of restaurant nearest their homes. Network distance was calculated in ArcGIS 9.3.1 and using ESRI StreetMap Premium North America NAVETQ 2009 Release 1 (2008), which determined a route based on driving time impedance. Thus, the distances (in miles) represented the fastest, but not necessarily the shortest route subjects would likely drive from home to the nearest fast food and quick service restaurant along the existing road network.

Logistic regression was used for binary health outcomes: self-reported health, being overweight, being obese, having CVD and having diabetes. Of the four modeling approaches, model 1 estimated the relationship between fast food or quick service use and health outcomes without the adjustment for any covariate. Model 2 added distance from home to the closest restaurant. Model 3 adjusted for demographic characteristics including gender, ethnicity, number of children under 12, number of children between 12 and 18 and household size. Socioeconomic status indicators (for example, income, education and employment) were included in model 4. Models 5–8 investigated the relationship between all the variables in model 4 with being overweight, being obese, having CVD and having diabetes, respectively. Interaction effects were also examined between socioeconomic status and fast food or quick service use on health outcomes after adjusting for demographic characteristics. Analyses were conducted using R, version 3.1 (GNU General Public License).

Descriptive analyses

The ⩾ 2 per week fast food or quick service user population (408) was smaller than that of <2 per week users (1584); nine respondents did not report on fast food or quick service use ( Table 1 ). Median distances to the closest fast food or quick service restaurants for frequent fast food or quick service users were 0.83 and 0.62 miles, respectively. Because the two distance measures were highly correlated ( r =0.86) and health effect analyses for either distance measure yielded similar results, reported model results only included the measure of distance to the closest quick service restaurant.

Fourteen percent of the participants reported being in perceived poor health; 56% were overweight; 21% were obese; 10% had a CVD; and 9% had diabetes. The majority of the population consisted of women (62%) and Whites (78%). Sixty-one percent were employed; 82% had at least some college education and 60% had an annual income of more than $50,000.

Using a fast food or quick service ⩾ 2 times per week significantly increased the risk of reporting being in perceived poor health, after adjusting for demographic characteristics and socioeconomic status ( Table 2 , model 4; odds ratio (OR) 1.61; 95% confidence interval (CI) 1.13–2.28). Increasing the distance to the closest quick service restaurant decreased the risk of reporting perceived poor health, but the association was not present after adjusting for socioeconomic status. Being employed, having higher educational attainment and a higher income decreased the risk of reporting perceived poor health.

Frequent fast food or quick service use significantly increased the risk of being overweight ( Table 3 , model 5; OR 1.66; 95% CI 1.28–2.16). Increasing age was marginally associated with the risk of being overweight, while being male was a strong predictor. Frequent fast food or quick service use was also associated with the risk of being obese ( Table 3 , model 6: OR 2.02; 95% CI 1.52–2.69). Having some college education and an income of >$50 000 decreased the risk of being obese. However, age and being male were not related to the risk of being obese. Distance to the closest fast food or quick service restaurant was not associated with being overweight or obese in any of the models.

Finally, using a fast food or quick service restaurant ⩾ 2 times per week was not associated with having CVD or diabetes ( Table 3 , model 7 and model 8). Increasing age was marginally associated with having these diseases. Being male was a strong predictor of increased risk of having CVD. Being unemployed also strongly increased the risk of having CVD or diabetes.

No interaction effects were found between fast food or quick service use and socioeconomic status for any of the health outcomes (data not shown). Distance to the closest fast food or quick service restaurant was also not significant in either models.

In line with past studies, the present analyses found that using a fast food restaurant or quick service restaurant ⩾ 2 times a week was significantly related with being in perceived poor health, overweight or obese, 3 , 6 but not with having CVD or diabetes. None of the health outcomes of frequent fast food and quick service use were associated with living close to such premises after adjusting for demographics and socioeconomic status. These results confirmed earlier findings that proximity to fast food and quick service restaurants did not seem to affect health. 17 , 18 , 19

In this study, the respondents’ food environment extended beyond that of both the home and the work neighborhoods: for the ⩾ 2 times per week users, the median network travel distance to the fast food and quick service restaurant that they reported using was 3.82 miles away from their home and 2.20 miles away from their work place. This was almost five times longer than the median distance to nearest such restaurant to their home (0.79 mile); and almost 16 times that same distance to the nearest such restaurant to their work (0.14 mile). In research done in Atlanta, GA, the trip to a fast food restaurant was similar, with a mean of 4.96 miles (s.d. 4.41), 40 pointing to the prevalence of car travel to these destinations. 41 In the present study, 65% of the respondents using a fast food or quick service restaurant ⩾ 2 times per week reported driving to it. These results indicated that fast food users were more likely to drive to these restaurants that were further away from their home or work place.

Clearly, populations with low levels of mobility might be more influenced by the environment near their home. Children living in the lower-income neighborhoods of UK and US towns with higher density of fast food outlets were more likely to be obese. 42 , 43 For adolescents, having exposure to poor quality food environments negatively affected eating patterns and had a positive relation to weight. 44 Proximity to fast food restaurants near home and school was also linked to a lower Healthy Eating Index. 45 Yet many studies of children and youth also found no association between proximity to fast food restaurants and health behaviors and outcomes, 46 , 47 even though there is evidence that fast food restaurants tend to cluster near schools in low-income neighborhoods. 48

The present findings do not rule out the possible influence of the food environment near home on out-of-home eating habits. Whatever the level of mobility, a person’s choice of an eating establishment might still be indirectly related to exposure to such restaurants near home, as, for example, to live close to such restaurants might reinforce the decision to eat at similar places. One recent study using travel data in Montreal, Canada, yielded mixed results considering the effects of exposure to fast foods on being overweight in both the residential and the 'non-residential' neighborhood. Future studies need to examine the complete spatial extent of food consumption habits, which includes the home- and work-based environments as well as any other environment where eating takes place.

As in many other studies, demographic and socioeconomic characteristics were strong predictors of the health outcomes examined in this study. The role of gender remains an important consideration. Frequent fast food and quick service users who were male had an increased risk of being overweight, but not being obese, with no effect of neighborhood exposure. A recent longitudinal study based on the Framingham Heart Study Offspring Cohort found that for females, associations between proximity to fast food restaurants and increased BMI were significant; but the study did not include measures of restaurant use. 49 In the present analyses, older age moderately predicted all of the outcomes, except for being obese. Higher socio-economic status was protective of being in perceived poor health; higher education attainment and income were protective of being obese, but not of being overweight, again with no home neighborhood exposure effect. At odds with the present study, a recent longitudinal study found that for low-income populations, proximity to fast food restaurants was associated with higher fast food consumption. 50 Again, however, no measure of restaurant use was available.

This study’s strengths lied in the availability of data about where people ate away from home, and in the individual-level analyses of behavior and environment. While environment was measured objectively, however, behavior and health outcomes were self-reported, thus subject to possible bias. 51 The individual-level analyses bypass the limitations of area-based measures used in other studies, 52 but the cross-sectional design means that the direction of associations is unknown.

Finally, the study comprised a sample of King County’s adult population. Populations living in areas that have a different distribution of health outcomes might yield different results. Also the number and spatial distribution of fast food restaurants may vary by region, thus affecting measures of exposure and access. 53

Conclusions

Eating ⩾ 2 times a week at a fast food or quick service restaurant is associated with perceived poor health, added weight and obesity. However, living close to such restaurants is not related to negative health outcomes. Eating out at these restaurants takes place beyond the home neighborhood. To further examine whether exposure and access to fast food and quick service restaurants influence eating out behaviors, studies will need to consider the complete spatial extent of food consumption habits.

Wang MC, Cubbin C, Ahn D, Winkleby MA . Changes in neighbourhood food store environment, food behaviour and body mass index, 1981-1990. Public Health Nut 2008; 11 : 963–970.

Article   Google Scholar  

Nielsen SJ, Siega-Riz AM, Popkin BM . Trends in energy intake in U.S. between 1977 and 1996: similar shifts seen across age groups. Obes Res 2002; 10 : 370–378.

Article   PubMed   Google Scholar  

Duffey KJ, Gordon-Larsen P, Steffen LM, Jacobs DR Jr., Popkin BM . Regular consumption from fast food establishments relative to other restaurants is differentially associated with metabolic outcomes in young adults. J Nutr 2009; 139 : 2113–2118.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Moore LV, Diez Roux AV, Nettleton JA, Jacobs DR, Franco M . Fast-food consumption, diet quality, and neighborhood exposure to fast food: the multi-ethnic study of atherosclerosis. Am J Epidemiol 2009; 170 : 29–36.

Article   PubMed   PubMed Central   Google Scholar  

Fowles ER, Timmerman GM, Bryant M, Kim S . Eating at fast-food restaurants and dietary quality in low-income pregnant women. West J Nurs Res 2011; 33 : 630–651.

Casey AA, Elliott M, Glanz K, Haire-Joshu D, Lovegreen SL, Saelens BE et al . Impact of the food environment and physical activity environment on behaviors and weight status in rural U.S. communities. Prev Med 2008; 47 : 600–604.

Morse KL, Driskell JA . Observed sex differences in fast-food consumption and nutrition self-assessments and beliefs of college students. Nutr Res 2009; 29 : 173–179.

Article   CAS   PubMed   Google Scholar  

Nielsen SJ, Siega-Riz AM, Popkin BM . Trends in food locations and sources among adolescents and young adults. Prev Med 2002; 35 : 107–113.

Fleischhacker SE, Evenson KR, Rodriguez DA, Ammerman AS . A systematic review of fast food access studies. Obes Rev 2011.

Larson N, Story M . A review of environmental influences on food choices. Ann Behav Med 2009; 38 : S56–S73.

Morland K, Wing S, Diez Roux A . The contextual effect of the local food environment on residents' diets: the atherosclerosis risk in communities study. Am J Public Health 2002; 92 : 1761–1767.

Morland KB, Evenson KR . Obesity prevalence and the local food environment. Health Place 2009; 15 : 491–495.

Zick CD, Smith KR, Fan JX, Brown BB, Yamada I, Kowaleski-Jones L . Running to the store? The relationship between neighborhood environments and the risk of obesity. Soc Sci Med 2009; 69 : 1493–1500.

Maddock J . The relationship between obesity and the prevalence of fast food restaurants: state-level analysis. Am J Health Promot 2004; 19 : 137–143.

Bodor J, Rice J, Farley T, Swalm C, Rose D . The association between obesity and urban food environments. J Urban Health 2010; 87 : 771–781.

Inagami S, Cohen DA, Brown AF, Asch SM . Body mass index, neighborhood fast food and restaurant concentration, and car ownership. J Urban Health 2009; 86 : 683–695.

Hickson DA, Diez Roux AV, Smith AE, Tucker KL, Gore LD, Zhang L et al . Associations of fast food restaurant availability with dietary intake and weight among African Americans in the Jackson Heart Study, 2000-2004. Am J Public Health 2011; 101 : S301–S309.

Pearce J, Hiscock R, Blakely T, Witten K . A national study of the association between neighbourhood access to fast-food outlets and the diet and weight of local residents. Health Place 2009; 15 : 193–197.

Crawford DA, Timperio AF, Salmon JA, Baur L, Giles-Corti B, Roberts RJ et al . Neighbourhood fast food outlets and obesity in children and adults: the CLAN Study. Int J Pediatr Obes 2008; 3 : 249–256.

Rundle A, Neckerman KM, Freeman L, Lovasi GS, Purciel M, Quinn J et al . Neighborhood food environment and walkability predict obesity in New York City. Environ Health Perspect 2009; 117 : 442–447.

Li F, Harmer P, Cardinal BJ, Bosworth M, Johnson-Shelton D, Moore JM et al . Built environment and 1-year change in weight and waist circumference in middle-aged and older adults: Portland Neighborhood Environment and Health Study. Am J Epidemiol 2009; 169 : 401–408.

Daniel M, Paquet C, Auger N, Zang G, Kestens Y . Association of fast-food restaurant and fruit and vegetable store densities with cardiovascular mortality in a metropolitan population. Eur J Epidemiol 2010; 25 : 711–719.

Morgenstern LB, Escobar JD, Sanchez BN, Hughes R, Zuniga BG, Garcia N et al . Fast food and neighborhood stroke risk. Ann Neurol 2009; 66 : 165–170.

Azuma AM, Gilliland S, Vallianatos M, Gottlieb R . Food access, availability, and affordability in 3 Los Angeles communities, Project CAFE, 2004-2006. Prev Chronic Dis 2010; 7 : A27.

PubMed   PubMed Central   Google Scholar  

Lovasi GS, Hutson MA, Guerra M, Neckerman KM . Built environments and obesity in disadvantaged populations. Epidemiol Rev 2009; 31 : 7–20.

Fraser LK, Edwards KL, Cade J, Clarke GP . The geography of Fast Food outlets: a review. Int J Environ Res Public Health 2010; 7 : 2290–2308.

Hemphill E, Raine K, Spence JC, Smoyer-Tomic KE Exploring obesogenic food environments in Edmonton, Canada: the association between socioeconomic factors and fast-food outlet access Am J Health Promot 2008; 22 : 426–432.

Smoyer-Tomic KE, Spence JC, Raine KD, Amrhein C, Cameron N, Yasenovskiy V et al . The association between neighborhood socioeconomic status and exposure to supermarkets and fast food outlets. Health Place 2008; 14 : 740–754.

Black JL, Macinko J, Dixon LB, Fryer JGE . Neighborhoods and obesity in New York City. Health Place 2010; 16 : 489–499.

Jones J, Terashima M, Rainham D . Fast food and deprivation in Nova Scotia. Can J Public Health 2009; 100 : 32–35.

Spence JC, Cutumisu N, Edwards J, Raine KD, Smoyer-Tomic K . Relation between local food environments and obesity among adults. BMC Public Health 2009; 9 : 192.

Hurvitz PM, Moudon AV, Rehm CD, Streichert LC, Drewnowski A . Arterial roads and area socioeconomic status are predictors of fast food restaurant density in King County, WA. Int J Behav Nutr Phys Act 2009; 6 : 46.

Sharkey JR, Johnson CM, Dean WR, Horel SA . Association between proximity to and coverage of traditional fast-food restaurants and non-traditional fast-food outlets and fast-food consumption among rural adults. Int J Health Geogr 2011; 10 : 37.

Timperio AF, Ball K, Roberts R, Andrianopoulos N, Crawford DA . Children's takeaway and fast-food intakes: associations with the neighbourhood food environment. Public Health Nutr 2009; 12 : 1960–1964.

Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs JDR et al . Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet 2005; 365 : 36–42.

Simon PA, Kwan D, Angelescu A, Shih M, Fielding JE . Proximity of fast food restaurants to schools: Do neighborhood income and type of school matter? Prev Med 2008; 47 : 284–288.

Wang MC, Gonzalez AA, Ritchie LD, Winkleby MA . The neighborhood food environment: sources of historical data on retail food stores. Int J Behav Nutr Phys Act 2006; 3 : 15.

US Census Bureau. North American Industry Classification System (NAICS) Definitions, Food and Beverage Stores. 2007. Available from http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=445&search=2007%20NAICS%20Search (Accessed 24 August 2011).

Vernez Moudon A, Drewnowski A, Duncan GE, Hurvitz PM, Saelens BE, Scharnhorst E . Characterizing the food environment: pitfalls and future directions. Public Health Nutr 2013; 16 : 1238–1243.

Kerr J, Frank L, Sallis JF, Saelens B, Glanz K, Chapman J . Predictors of trips to food destinations. Int J Behav Nutr Phys Act 2012; 9 : 58.

Thornton LE, Crawford DA, Cleland VJ, Timperio AF, Abbott G, Ball K . Do food and physical activity environments vary between disadvantaged urban and rural areas? Findings from the READI Study. Health Promot J Austr 2012; 23 : 153–156.

Frazier DA . The link between fast food and the obesity epidemic. Health Matrix Clevel 2007; 17 : 291–317.

PubMed   Google Scholar  

Oreskovic NM, Winickoff JP, Kuhlthau KA, Romm D, Perrin JM . Obesity and the built environment among Massachusetts children. Clin Pediatr (Phila) 2009; 48 : 904–912.

Davis B, Carpenter C . Proximity of fast-food restaurants to schools and adolescent obesity. Am J Public Health 2009; 99 : 505–510.

He M, Tucker P, Irwin JD, Gilliland J, Larsen K, Hess P . Obesogenic neighbourhoods: the impact of neighbourhood restaurants and convenience stores on adolescents' food consumption behaviours. Public Health Nutr 2012; 15 : 2331–2339.

Richardson AS, Boone-Heinonen J, Popkin BM, Gordon-Larsen P . Neighborhood fast food restaurants and fast food consumption: a national study. BMC Public Health 2011; 11 : 543.

Powell LM . Fast food costs and adolescent body mass index: evidence from panel data. J Health Econ 2009; 28 : 963–970.

Kestens Y, Daniel M . Social inequalities in food exposure around schools in an urban area. Am J Prev Med 2010; 39 : 33–40.

Block JP, Christakis NA, O'Malley AJ, Subramanian SV . Proximity to food establishments and body mass index in the Framingham Heart Study offspring cohort over 30 years. Am J Epidemiol 2011; 174 : 1108–1114.

Boone-Heinonen J, Gordon-Larsen P, Kiefe CI, Shikany JM, Lewis CE, Popkin BM . Fast food restaurants and food stores: longitudinal associations with diet in young to middle-aged adults: the CARDIA study. Arch Intern Med 2011; 171 : 1162–1170.

Idler E, Benyamini Y . Self-rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior 1997; 38 : 21–37.

Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS . The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place 2010; 16 : 175–190.

Raja S, Ma C, Yadav P . Beyond food deserts: measuring and mapping racial disparities in neighborhood food environments. J Planning Educ Res 2008; 27 : 469–482.

Download references

Acknowledgements

This work was supported by NIH/NIDDK R01DK076608, Food environment, diet quality and disparities in obesity.

Author information

Authors and affiliations.

Graduate Program in Community and Regional Planning, School of Architecture, University of Texas at Austin, Austin, TX, USA

Department of Urban Design and Planning, College of Built Environments, University of Washington, Seattle, WA, USA

A V Moudon & P M Hurvitz

Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA

Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA

A Drewnowski

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to J Jiao .

Ethics declarations

Competing interests.

Dr Drewnowski advises McDonald’s Corporation on global issues related to public health nutrition.

Rights and permissions

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Reprints and permissions

About this article

Cite this article.

Jiao, J., Moudon, A., Kim, S. et al. Health Implications of Adults’ Eating at and Living near Fast Food or Quick Service Restaurants. Nutr & Diabetes 5 , e171 (2015). https://doi.org/10.1038/nutd.2015.18

Download citation

Received : 28 January 2015

Revised : 25 April 2015

Accepted : 03 May 2015

Published : 20 July 2015

Issue Date : July 2015

DOI : https://doi.org/10.1038/nutd.2015.18

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Area characteristics and consumer nutrition environments in restaurants: an examination of hispanic caribbean restaurants in new york city.

• Melissa Fuster
• Hanish Kodali
• Glen Johnson

Journal of Racial and Ethnic Health Disparities (2022)

Built environmental characteristics and diabetes: a systematic review and meta-analysis

• N. R. den Braver
• J. Lakerveld
• J. W. J. Beulens

BMC Medicine (2018)

Examining the interaction of fast-food outlet exposure and income on diet and obesity: evidence from 51,361 UK Biobank participants

• Thomas Burgoine
• Chinmoy Sarkar
• Pablo Monsivais

International Journal of Behavioral Nutrition and Physical Activity (2018)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Search Menu
• Sign in through your institution
• Advance articles
• Editor's Choice
• Supplement Archive
• Article Collection Archive
• Author Guidelines
• Submission Site
• Open Access
• Call for Papers
• Why Publish?
• About Nutrition Reviews
• About International Life Sciences Institute
• Editorial Board
• Early Career Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, energy and energy density, relationship between takeaway and fast foods and obesity, energy content and intake of takeaway and fast foods, total fat and saturated fatty acids, trans fatty acids.

• < Previous

Nutritional challenges and health implications of takeaway and fast food

• Article contents
• Figures & tables
• Supplementary Data

Agnieszka Jaworowska, Toni Blackham, Ian G Davies, Leonard Stevenson, Nutritional challenges and health implications of takeaway and fast food, Nutrition Reviews , Volume 71, Issue 5, 1 May 2013, Pages 310–318, https://doi.org/10.1111/nure.12031

• Permissions Icon Permissions

Consumption of takeaway and fast food continues to increase in Western societies and is particularly widespread among adolescents. Since food is known to play an important role in both the development and prevention of many diseases, there is no doubt that the observed changes in dietary patterns affect the quality of the diet as well as public health. The present review examines the nutritional characteristics of takeaway and fast food items, including their energy density, total fat, and saturated and trans fatty acid content. It also reports on the association between the consumption of such foods and health outcomes. While the available evidence suggests the nutrient profiles of takeaway and fast foods may contribute to a variety of negative health outcomes, findings on the specific effects of their consumption on health are currently limited and, in recent years, changes have been taking place that are designed to improve them. Therefore, more studies should be directed at gaining a firmer understanding of the nutrition and health consequences of eating takeaway and fast foods and determining the best strategy to reduce any negative impact their consumption may have on public health.

Lifestyle changes that have taken place in many countries worldwide over the last few decades have been shown to impact food consumption patterns. 1 , – 4 One of the most prominent trends is the increasing frequency with which meals are consumed outside of the home environment. 4 , – 6 In addition, even meals consumed at home are often purchased from catering outlets that offer takeaway or home delivery service. 5 , 7 The traditional family dinner is increasingly being replaced by eating “on the run” at various locations throughout the day. 1 As a result, less time is spent on food preparation, with an average woman and man in the United States spending 47 min and 19 min per day, respectively, carrying out food preparation and cleaning up. Moreover, among the 13,200 US citizens in one study, 59% of men and 32% of women did not spend any time on daily food preparation. 8

Food eaten outside of the home is becoming an important and regular component of the Western diet. 9 , 10 A number of studies have shown increased frequency of takeaway and fast food consumption worldwide, especially in Europe, the United States, and Australia. 9 , – 15 A governmental report in the United Kingdom indicated about 22% of residents were found to purchase foods from takeaway outlets at least once a week and 58% a few times a month. 11 A similar frequency of consumption of takeaway or fast food has also been observed in other countries, with approximately 28% of Australians 12 consuming takeaway meals at least twice a week and 37% of US residents 13 eating fast food at least once over two nonconsecutive days. Fast food is particularly popular among adolescents, with a report from 2001 indicating that 75% of US teenagers between the ages of 11 and 18 years eat at fast-food outlets at least once a week 14 and a 2010 report indicating that 70% of Brazilian students (9–18 years old) consume fast food four times or more per week. 15 Moreover, Guthrie et al. 10 reported that consumption of fast food among children has increased from 2% of total energy in the 1970s to 10% of energy in the 1990s. 10 That observed trend is continuing among children and adolescent populations, with data from the 2003–2006 National Health and Nutrition Examination Surveys showing a further increase to 13% of total daily energy intake. 16

It is well known that food plays an important role in the development and prevention of many diseases. 17 There is also no doubt that observed changes in dietary patterns affect the quality of the diet as well as public health. Consumption of takeaway and fast food has been shown to have adverse health effects, and while the majority of studies on this subject have focused on the relationship between fast food consumption and weight gain, 18 , – 20 more frequent consumption of meals prepared outside of the home has also been observed to correspond with increased risk of insulin resistance, type 2 diabetes, elevated total cholesterol, and low-density lipoprotein cholesterol (LDL-C) levels as well as decreased high-density lipoprotein cholesterol (HDL-C) concentrations. 18 , 21 , 22 Takeaway or fast food consumers are characterized by higher intakes of energy, fat, saturated fatty acids (SFAs), trans fatty acids (TFAs), added sugar and sodium, and lower intakes of fiber, macronutrients, and vitamins in comparison to those who do not eat food prepared outside the home. 13 , 19 , 23 , 24 Additionally, takeaway and fast-food consumption has been linked to poor dietary patterns including higher intakes of carbonated soft drinks and sweets and lower consumption of fruits, vegetables, whole grains, and dairy products. 12 , 13 , 19 , 24

This review focuses on the energy and fat content in takeaway and fast foods and their health implications. However, it should be pointed out that other components of takeaway and fast foods, such as salt and sugar, also have important effects on health.

Literature searches were performed using the following electronic databases: Medline, ScienceDirect, and Web of Science. The following key words were used: fast food, takeaway food, nutrient content, lifestyle, health, obesity, cardiovascular disease, blood lipids, fat, saturated fatty acids, trans fatty acids, energy density, food consumption patterns, diet quality. In addition, the reference list of each original and review article identified was searched for additional references. Searches were restricted to manuscripts in the English-language literature and included all available data until March 2011. Articles were limited to human participants only.

Humans possess a weak initial ability to recognize the energy density of consumed food and to appropriately regulate the bulk of food eaten to maintain energy balance; thus, people generally tend to consume a similar amount of food every day regardless of variations in energy density. 25 , – 27 This tendency to consume a constant amount of food was confirmed by Seagle et al., 28 who analyzed the 4-day food records of normal-weight adults. Daily variations in the weight of consumed food were significantly smaller than variations in the intake of either energy or fat. 28 Similarly, a retrospective investigation of three community studies from the United Kingdom (the Cambridge Family Food Survey, n  = 195); the MRC National Survey of Health and Development [NSHD], n  = 343; and the Leeds Nutritional Survey, n  = 2,086) showed that the weight of food consumed remained relatively constant over a 7-day period. 29 However, when food with a low energy density is eaten, a greater amount of food needs to be consumed for a given level of energy intake in comparison to food with a high energy density. Therefore, increasing the energy density of the diet may result in a passive increase in energy intake, because people are generally habituated to eat a relatively constant weight of food.

Bell et al. 26 conducted a study of normal-weight women ( n  = 18) who consumed all of their meals in the laboratory over three 2-day periods. During lunch, dinner, and an evening snack, participants consumed ad libitum main entrees, which were similar in macronutrient composition but varying in energy density (low, medium, or high). The women consumed similar amounts of food independent of the energy density of the meal served. Thus, energy intake was about 25% lower with meals of low energy density in comparison with those of high energy density. Results showed no differences across conditions in hunger or fullness before meals, after meals, or over each 2-day period. 26 These findings were confirmed by several other studies that tested the effect of variations in the fat content of the diet while maintaining a constant energy density. 30 , 31 Stubbs et al., 30 in a 14-day intervention study, reported that men who were offered a diet varying in fat content (20, 40, and 60% of total energy) but with a constant energy density, ate a constant weight of food; therefore, they had similar energy intakes despite different proportions of fat content in the diet. Similarly, in a randomized crossover study performed over an 11-day period, Saltzman et al. 31 found that seven pairs of male twins who consumed, ad libitum , a low- or a high-fat diet matched for energy density (20% or 40% of total energy) had similar daily energy intakes (10.3 and 10.7 MJ/d, respectively) regardless of the condition of the diet. These findings support the hypothesis that the energy density of consumed food is a crucial determinant of energy intake. Therefore, the weight or volume of food consumed, and, thus, the energy density, may increase or decrease energy intake independent of the macronutrient content of the diet.

The relationship between fast or takeaway food consumption and increased body mass index (BMI) and obesity has been reported in many epidemiological studies. 18 , – 20 , 32 Among a Spanish population ( n  = 3,054), Schröder et al. 33 found that consumption of fast food more frequently than once a week increased the risk of being obese by 129%. These results are consistent with the findings of Kjøllesdal et al., 34 who reported from a group of working Oslo citizens ( n  = 8,943) that the likelihood of being obese increased significantly with frequent eating in staff canteens, after demographic and socioeconomic variables were taken into account. Similar trends have also been observed in developing countries; Rouhani et al. 35 found that high intakes of fast foods were significantly associated with increased incidences of overweight and obesity among Isfahani (Iranian) girls aged 11–13 years.

Furthermore, consumption of fast foods two times or more per week has been independently associated with a 31% higher prevalence of moderate abdominal obesity in men and a 25% higher prevalence in women. 19 According to a theoretical model, an energy increase of 17 kcal/day for men and 19 kcal/day for women would lead to a weight increase of 1 kg per year independent of baseline body weight. 36 On average, regular consumption of fast-food meals was related to increases in energy intake of 56 kcal/day 37 and 187 kcal/day 19 among adults and children, respectively. Thus, a higher frequency of fast-food consumption was associated with a weight gain of 0.72 kg over 3 years, 37 and of 4.5 kg over a 15-year period, 18 above the average weight gain. Moreover, women who reported eating takeaway food once a week were 15% less likely to be weight maintainers than those who rarely (once a month or less) or never ate takeaway food. 38

It has been shown that a typical meal purchased from fast-food restaurants tends to be energy dense and contains approximately 236 kcal/100 g, which is twice the recommended energy density of a healthy diet. 39 Considering the large portion sizes of meals eaten out of the home, one meal can provide approximately 1,400 kcal. 40 Bauer et al. 41 found that despite the increasing attention to the role of fast food in the American diet, including legislation and public health campaigns addressing the healthfulness of fast food, the median energy content across all menu items remained relatively stable over a 14-year study period (1997–2010).

Mancino et al. 42 based on the dietary recall data collected over2 nonconsecutive days from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) and the 1994–1996 Continuing Survey of Food Intakes by Individuals, and with the use of a first-difference estimator, found that each meal eaten away from home added, on average, 130 kcal to total daily energy intake, with lunch and dinner having the greatest effect on total daily energy. French et al., 14 in a study conducted among 11–18-year-old American teenagers ( n  = 4,746), reported that energy intake was 40% higher among male and 37% higher among female adolescents who reported eating fast food three times or more during the studied week in comparison with those who had not eaten fast foods. Additionally, a dose-response pattern was observed with energy intake directly increasing with increased frequency of fast food consumption. 14 Similarly, a follow-up study including African American women aged 30–69 years ( n  = 44,072) indicated that, compared to women who have never eaten Chinese food, pizzas, fried fish, fried chicken, or burgers, women who reported eating such foods at least once a week had significantly higher daily energy intakes. 21 Furthermore, a study by Bowman and Vinyard 19 that included 9,872 adults aged 20 years and older showed a positive relationship between the energy density of the diet and fast food consumption. Their evaluation of the quality of the diet of American adults showed increased dietary energy density among men and women who reported eating fast food (95 and 102 kcal/100 g, respectively) compared to those who did not (89 and 98 kcal/100 g among men and women, respectively). 19

The high levels of fat intake commonly associated with takeaway or fast food consumption may be a factor leading to obesity development that is independent of total energy intake. Findings of a study carried out by Alfieri et al. 43 among 150 adults in the United Kingdom found a strong positive correlation between BMI and total fat consumption but no association with energy intake. These results were in line with findings of a cross-sectional study of 15,266 men (55–79 years) performed by Satia-Abouta et al., 44 which showed that fat intake has a higher adipogenic effect than total energy intake. In a multivariate linear regression model after adjustment for demographic and health-related characteristics, BMI increased by 0.14 and 0.53 kg/m 2 for every 500 kcal of total energy intake and 500 kcal energy derived from fat, respectively. Additionally, only energy provided from fat, but not energy from other macronutrients (carbohydrate and protein), increased linearly with increasing BMI. In contrast, Larson et al. 45 suggested that dietary fat plays a very minor role in increasing adiposity, and explained only 2% of variation in body fat after controlling for other obesity risk factors.

There are several possible explanations for why dietary fat intake may be associated with body weight gain. A number of studies have shown that fat exerts a less satiating effect than either carbohydrate or protein. Cotton et al. 46 found that a carbohydrate-supplemented breakfast (173.4 g of carbohydrate, 11.2 g of fat, 12.7 g of protein, and 803 kcal of total energy) suppressed intake of food with the next meal, in contrast to the breakfast supplemented by fat (77.8 g of carbohydrate, 50.9 g of fat, 12.7 g of protein, and 803 kcal total energy), which produced no detectable effect on appetite expression. Furthermore, fat is utilized with very high energy efficiency; thus, the diet-induced thermogenesis following fat consumption is much lower than after protein or carbohydrate intakes. Also, a high-fat meal does not enhance lipid oxidation, and may, therefore, promote dietary fat accumulation in adipose tissue. In a study by Bennett et al., 47 the addition of 50 g of fat to a standard breakfast (55% energy from carbohydrate, 30% from fat, and 15% from protein) did not increase fat oxidation or energy expenditure either during the immediate 6 h postprandial period or over the following 18 h. Similarly, Horton et al. 48 found that in a group of 16 men who were offered 14 days of isoenergetic overfeeding (50% above energy recruitment) of fat and carbohydrate, overfeeding with fat did not produce an increase in fat oxidation and total energy expenditure, and led to storage of 90–95% of excess energy. In contrast, carbohydrate overfeeding was associated with increased carbohydrate oxidation and total energy expenditure and resulted in 75–85% of excess energy being stored. 48 Furthermore, Raben et al. 49 in a study of 19 healthy participants who were provided with meals similar in energy density but rich in protein, fat, carbohydrate, or alcohol observed that postprandial lipid oxidation was suppressed after protein-, carbohydrate-, and alcohol-rich meals and was almost unchanged after the fat-rich meal. Griffiths et al. 50 reported that lipid oxidation was higher after a high-fat meal (80 g of carbohydrate, 80 g of fat, and 18 g of protein) than after a low-fat meal (80 g of carbohydrate, 0.8 g of fat, and 18 g of protein), but the difference in oxidation level reached 10 g only (20.7 vs. 10.6 g, P  < 0.01), despite the high-fat meal providing 79.2 g more fat than the low-fat meal. It should also be mentioned that fat is more effectively absorbed from the gastrointestinal tract in comparison to carbohydrate. Lammert et al. 51 showed that a high-fat diet produced significantly lower fecal loss of energy than a high-carbohydrate diet. In addition, fat is known to improve the taste and texture of many food products, which may also promote active overconsumption associated with enhanced appetite due to sensory stimulation. 52 Increased food intake occurring with increased food palatability has been observed in many previous studies. 53 , – 56 However, other studies that investigated the sensory properties of food involved in sensory-specific satiety found that increased sensory stimulation may reduce food consumption. 57

A diet high in fat, particularly one rich in SFAs, may not only lead to a higher risk of obesity development, it may also have other adverse health effects. Generally, SFAs increase total and HDL-C levels, although not all SFAs affect plasma lipid and lipoprotein concentrations in the same manner. 58 For example, stearic acid, in comparison with other SFAs, has little effect on plasma lipids; this has been proposed to be a result of the rapid conversion of stearic acid in the body to oleic acid. 59 On the other hand, SFAs with 12–16 carbon atoms are considered to be hypercholesterolemic, and lauric acid (C12:0) appears to be more potent than myristic acid (C14:0) or palmitic acid (C16:0). 59 However, it has been found that despite increasing serum total and LDL-C levels, C12:0, C14:0, and C16:0 acids also increase the concentration of HDL-C; as a result, they do not increase the ratio of total cholesterol to HDL-C. 60 Whether a diet high in SFAs is associated with an increased risk of coronary heart disease is still controversial. 60 , 61 A number of epidemiological and dietary intervention studies have found that a diet rich in SFAs is associated with a higher risk of impaired glucose tolerance, insulin resistance, and type 2 diabetes, 62 , – 64 but there is no evidence of a direct causal relationship with CVD. 65 Thanopoulou et al., 62 in a multinational survey, found that participants with recently diagnosed and undiagnosed type 2 diabetes had higher intakes of SFAs compared with healthy controls. These findings were similar to those of Wang et al. 64 who, in a 9-year follow-up study of 2,909 American participants (45–64 years of age), showed a positive association between diabetes incidence and the proportion of total SFAs in plasma cholesterol esters and phospholipids, which reflects dietary intake of fatty acids. In addition, higher intake of SFAs may increase the risk of several cancers. Kurahashi et al. 66 in a 7.5-year follow-up study among 43,435 Japanese men aged 45–74 years found that myristic and palmitic acids increased the risk of prostate cancer in a dose-dependent manner. Multivariable relative risk on comparison of the highest with the lowest quartiles of myristic acid and palmitic acid intake were 1.62 (1.15–2.29) and 1.53 (1.07–2.20), respectively. There is also evidence suggesting a possible relationship between SFA intake and a modest increase in breast cancer risk. 67

One of the main sources of SFAs in takeaway or fast foods worldwide is palm oil, which is widely used as a frying medium due to excellent frying performance together with production of a highly desirable fried food flavor; it is especially popular in Southeast Asian countries, as well as in small, independent takeaway outlets in the United Kingdom. 68 , 69 Palm oil is suggested as an acceptable alternative to PHVO in the deep fat frying process, but unhydrogenated vegetable oils are recommended as they produce a much more favorable plasma/serum lipid profile than either palm oil or partially hydrogenated oils. 59 , 70 In a dietary intervention study by Vega-López et al., 70 15 participants were provided for 5 weeks with food varying in the type of fat (partially hydrogenated soybean oil, soybean oil, palm oil, or canola oil; at two-thirds of total fat, or 20% of total energy). It was found that both partially hydrogenated soybean and palm oil resulted in higher LDL-C concentrations than other investigated fats. No significant differences in the total cholesterol to HDL-C ratio were observed among the diets enriched with palm, canola, and soybean oils. Vessby et al., 71 in the KANWU study, included 162 healthy participants who received an isoenergetic diet for 3 months containing either a high proportion of saturated or monounsaturated fatty acids, and found that replacement of SFAs with monounsaturated fatty acids was associated with improved insulin sensitivity.

On average, food eaten out of the home is characterized by a high total fat and SFA content. Stender et al., 72 after analyzing meals containing french fries and fried chicken (nuggets or hot wings) purchased from McDonald's and KFC outlets in 35 countries worldwide, found that the total fat content varied from 41 to 74 g depending on the country. These results were supported by later findings of Dunford et al., 73 who reported that food items (burgers, chicken products, sides, or pizzas) purchased from fast-food chains contained between 10 and 13 g of total fat and between 3.9 and 4.9 g of SFAs per 100 g.

The intake of fat and SFAs increases along with higher frequency of out-of-home eating. 13 , 19 , 21 A study involving a large sample ( n  = 44,072) of adult African American women showed that total fat intake was significantly higher among women who reported eating out of the home at least once a week, regardless of the type of meals consumed (burgers, fried chicken, fried fish, Chinese food, pizzas, or Mexican food) when compared to those who had never eaten food prepared outside the home. 21 This is consistent with previous findings of Paeratakul et al., 13 which indicated that, among 9,063 adults and 8,307 children and adolescents, on the day when fast food was eaten, the intake of total fat, SFAs, and percentage of energy provided by fat was higher compared to the day without fast food consumption.

Trans fatty acids are formed during the commercial partial hydrogenation of unsaturated fats. Small amounts of TFAs are also produced by ruminants during the biohydrogenation of unsaturated fatty acids from feed by hydrogen produced during the oxidation of substrates with bacterial enzymes in the rumen. These two sources of TFAs contain similar species of TFA isomers, but in different amounts and proportions; thus, their consumption may have different biological effects. 74 The concentration of TFAs in partially hydrogenated vegetable oils (PHVO) may be as high as 30–50%, compared with only around 5% in dairy and ruminant meat products. 74 Ruminant and industrially produced TFAs have been shown to have a detrimental effect on blood lipids when consumed in high doses; however, moderate intakes of ruminant TFAs, such as those seen with normal dietary consumption, have neutral effects on plasma lipids and other risk factors for cardiovascular disease. 75 Hulshof et al. 76 reported that TFA intake from ruminant products was under 2 g/day (<1% of total energy intake) in all Western European countries investigated in the TRANSFAIR study, and the main source of TFAs in the diet was PHVO. A more recent study estimated that average US consumption of industrially produced TFAs has significantly decreased from 4.6 g/per day (2003) to 1.3 g/per day (2010) as a result of food labelling and legislation. 77 However, individuals with certain dietary habits may still consume high levels of industrially produced TFAs. 77 Indeed, very high TFA intake levels (3.5–12.5% of total energy intake) have been shown in males aged 12–19 years, and the types of foods consumed included fast food such as french fries, pies, and pastries. 78 In the United Kingdom, the National Diet Nutrition Survey reported an intake of less than 2 g per day of TFA, 79 but this survey did not assess intake from takeaway food from independent outlets.

Trans fatty acids, due to their physiological effects, are undesirable components of the diet. A growing body of evidence has demonstrated numerous adverse effects associated with consumption of TFAs, including systemic inflammation, diabetes, insulin resistance, endothelial dysfunction, obesity, decreased LDL particle size, decreased HDL-C and apolipoprotein A1 concentrations, and increased total cholesterol, lipoprotein (a) and apolipoprotein B levels. 80 , 81 A recent meta-analysis of the effects of TFA consumption on blood lipids and lipoproteins showed that each 1% energy replacement of TFAs with SFAs, monounsaturated fatty acids, or polyunsaturated fatty acids, respectively, decreased total cholesterol/HDL-C ratio by 0.31, 0.54, and 0.67; apolipoprotein B/apolipoprotein A1 ratio by 0.007, 0.010, and 0.011; and Lp(a) concentration by 3.76, 1.39, and 1.11 mg/L. 82 Esmaillzadeh et al. 83 in a cross-sectional study of 486 apparently healthy women aged 40–60 years found that greater consumption of PHVO was associated with increased circulating concentrations of several markers of endothelial dysfunction and systemic inflammation. C-reactive protein, interleukin 6, and soluble tumor necrosis factor 2 levels were, respectively, 73%, 17%, and 5% higher among women in the highest quintile of TFA intake compared with the lowest quintile. 84 Many studies that investigated an association between habitual intakes of or exposure to TFAs, assessed using tissue biomarkers (for example, erythrocyte membrane TFA concentrations, serum phospholipids, and adipose tissue fatty acid composition) have demonstrated a significantly increased risk of coronary heart disease among individuals with greater TFA consumption or exposure. 85 The majority of these studies have focused on PHVO and, until recently, no positive association between ruminant TFA consumption and cardiovascular risk was found 86 ; however, a recent study by Laake et al. 87 found ruminant TFAs increased this risk, including for coronary heart disease, among women but not among men. Furthermore, TFA consumption may be associated with weight gain and visceral fat accumulation. A large prospective study conducted among 16,587 men, after controlling for potential confounders, found that substitution of each 2% of energy intake from TFAs by energy from polyunsaturated fatty acids was independently associated with a 2.7 cm increase in waist circumference over 9 years. 88 It should also be mentioned that TFAs are transferred from the mother to the fetus across the placenta and are present in breast milk. 80 , 89 Because humans do not synthesize TFA isomers, the concentration of these isomers in human milk is directly related to the maternal diet. The content of TFAs in human milk varies among countries, from 0.5% in Africa, through 1.40–2.80% in Poland, to 6–7% of total fatty acids in Canada. 90 A recent cross-sectional study found that infants of mothers who consumed 4.5 g or more of TFAs daily while breastfeeding were over two times more likely to have body fat higher than 24% in comparison to the offspring of mothers consuming less TFAs. 91

Takeaway and fast food, particularly french fries and deep-fried meats may contain a large amount of TFAs from PHVO, which are used for deep frying. It has been reported that a single meal of french fries (171 g) and fried chicken (160 g) purchased from fast food outlets provided from 0.3 to 24 g of TFAs. 72 Similarly, Wagner et al. 92 found that the TFA content in burgers may vary between 0.1 and 1.05 g per 100 g and in french fries between 0.1 and 1.6 g per 100 g. This is in agreement with Australian data reporting that the level of TFAs in takeaway meals may be between 0.1 and 1.4 g/100 g depending on the type of meal. 93 Also, recent data from the United States showed that the content of TFAs in 32 representative fast-food samples ranged from 0.1 to 3.1 g per serving. 94

It has been assessed that individuals who frequently consume fast-food meals could be receiving between 6 and 12% of their dietary energy from TFAs, 95 and a single meal of fried chicken with chips may deliver four times more TFAs than the daily recommended allowance in the United Kingdom (i.e., no more than 2% of total recommended daily energy intake). 96

In New York City, legal requirements regarding the use of PHVO in the preparation of foods sold by chain and nonchain restaurants have been implemented. Phase one of the initiative (2007) obligated all food outlets to use oils, shortenings, and margarines containing less than 0.5 g of TFAs per serving. Phase two (2008) involved reformulating all food items to contain less than 0.5 g of TFAs per serving. These restrictions have resulted in significant decreases in the TFA content of foods purchased, and between 2007 and 2009, the mean TFA content per purchase decreased by 2.4 g. However, the existing regulations in New York City apply only to restaurants that are required to hold a permit from the New York City Health Department 97 ; thus, the level of TFAs in food products from independent outlets may be higher. 98

It should be pointed out that most of the accessible studies regarding TFA levels in fast foods or other takeaway fried meal options did not distinguish between naturally occurring TFAs in food products and TFAs from PHVO, or they evaluated the levels of specific species of TFA isomers.

In summary, a growing body of evidence suggests that, even though positive changes are being made to improve the nutrient profiles of takeaway and fast foods, 77 some of these frequently consumed foods may contribute to a variety of negative health outcomes, including cardiovascular disease, insulin resistance, type 2 diabetes, and obesity. 12 , 18 , – 22 Simultaneously, food prepared outside of the home is making up an increasing portion of the Western diet and there is no expectation that this trend will reverse or stop. However, most of the studies performed to date have only investigated the nutritional quality of food from fast-food restaurant chains, and there is still a lack of data regarding the nutrient content in takeaway meals from small independent outlets, including those serving such foods as ethnic cuisines, deli foods, fish and chips, and pizza. Furthermore, there is a lack of good-quality data on the consumption of different takeaway food options. To the best of our knowledge, no studies published to date have differentiated between consumption of fast foods and other types of takeaway meals, and the majority of previous studies have concentrated on foods from fast-food chain restaurants or have investigated food prepared outside of the home without considering the source, i.e., fast-food chain restaurants or independent takeaway outlets. However, recent work indicates there are significant differences in the nutrient composition of different types of takeaway meals (e.g., Indian, Chinese, English, pizzas, kebabs) 99 , 100 as well as between takeaway meals and ready-to-eat meal options of a similar type. 101 , 102 To date, only one study has examined the relationship between the frequency of consumption of specific types of meals eaten out of the home (i.e., burgers, pizzas, fried chicken, fried fish, Chinese food, and Mexican food) and the incidence of type 2 diabetes, but this study was limited to restaurant food only. 21 Furthermore, most studies have only investigated the frequency of out-of-home eating and have not taken into account the amount of food consumed, the overall diet quality, and other lifestyle factors. Therefore, more studies should be directed at furthering understanding of the nutrition and health consequences of both takeaway and fast food consumption and to finding the best strategies to reduce any negative impact their consumption may have on public health. Such strategies may require governmental regulation. In Finland, for example, legislation on food labelling, such as the mandatory warning of “high salt product” on products in which the salt concentration exceeds set limits, has been shown to be a useful tool to reduce salt intake in the population. 103 Similarly, in Denmark, the restriction of industrially produced TFA levels of all food products to a maximum of 2% of the total fat content showed it is possible to reduce or remove TFAs from the content of food products. 104

The cooperation of food technologists, nutritionists, and chefs is needed in order to alter the food preparation processes at fast-food and takeaway outlets with the goal of improving the nutritional quality of prepared meals. However, this may not be easy to achieve, as chefs and other decision makers may be reluctant to change recipes, especially due to concerns about adverse effects on palatability, which can potentially affect profits. Furthermore, voluntary guidelines do not always result in adequate changes to the nutritional quality of takeaway foods; thus, governmental regulations may be a more powerful means of effecting change.

Declaration of interest

The authors have no relevant interests to declare.

Jabs JT Devine CM . Time scarcity and food choices: an overview . Appetite. 2006 ; 47 : 196 – 204 .

Google Scholar

Naska A Fouskakis D Oikonomou E , et al. Dietary patterns and their socio-demographic determinants in 10 European countries: data from the DAFNE databank . Eur J Clin Nutr. 2006 ; 60 : 181 – 190 .

Uauy R Monteiro CA . The challenge of improving food and nutrition in Latin America . Food Nutr Bull. 2004 ; 25 : 175 – 182 .

Kearney J . Food consumption trends and drivers . Philos Trans R Soc Lond B Biol Sci. 2010 ; 365 : 2793 – 2807 .

Bowers DE . Cooking trends echo changing roles of women . Food Rev. 2000 ; 23 : 23 – 29 .

Hawkes C . Marketing activities of global soft drink and fast food companies in emerging markets: a review . In: Globalization, Diets and Noncommunicable Diseases . Geneva, Switzerland : World Health Organization ; 2002 . Available at: http://whqlibdoc.who.int/publications/9241590416.pdf . Accessed 27 November 2011.

Google Preview

Jekanowski MD . Causes and consequences of fast food sales growth . Food Rev. 1999 ; 22 : 11 – 16 .

Denton SL. Adding eldercare questions to the American Time Use Survey. Monthly Labor Review Online. November 2012. Available at: http://www.bls.gov/opub/mlr/2012/11/art3full.pdf . Accessed 21 March 2013.

Orfanos P Naska A Trichopoulos D , et al. Eating out of home and its correlates in 10 European countries. The European Prospective Investigation into Cancer and Nutrition (EPIC) study . Public Health Nutr. 2007 ; 10 : 1515 – 1525 .

Guthrie JF Lin BH Frazao E . Role of food prepared away from home in the American diet, 1977–78 versus 1994–96: changes and consequences . J Nutr Educ Behav. 2002 ; 34 : 140 – 150 .

Food Standards Agency . Consumer Attitudes to Food Standards: Wave 8 . January 2008 ; Available at: http://tna.europarchive.org/20111116080332/http://www.food.gov.uk/multimedia/pdfs/cas2007ukreport.pdf . Accessed 23 March 2013.

Smith KJ McNaughton SA Gall SL , et al. Takeaway food consumption and its associations with diet quality and abdominal obesity: a cross-sectional study of young adults . Int J Behav Nutr Phys Act. 2009 ; 6 : 29 .

Paeratakul S Ferdinand DP Champagne CM , et al. Fast-food consumption among US adults and children: dietary and nutrient intake profile . J Am Diet Assoc. 2003 ; 103 : 1332 – 1338 .

French SA Story M Neumark-Sztainer D , et al. Fast food restaurant use among adolescents: associations with nutrient intake, food choices and behavioural and psychosocial variables . Int J Obes. 2001 ; 25 : 1823 – 1833 .

Cimadon HM Geremia R Pellanda LC . Dietary habits and risk factors for atherosclerosis in students from Bento Gonçalves (state of Rio Grande do Sul) . Arq Bras Cardiol. 2010 ; 95 : 166 – 172 .

Poti JM Popkin BM . Trends in energy intake among US children by eating location and food source, 1977–2006 . J Am Diet Assoc. 2011 ; 111 : 1156 – 1164 .

World Health Organization . Diet, Nutrition, and the Prevention of Chronic Diseases . Technical Report Series 916. Geneva, Switzerland: World Health Organization; 2003 .

Pereira MA Kartashov AI Ebbeling CB , et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis . Lancet. 2005 ; 365 : 36 – 42 .

Bowman SA Vinyard BT . Fast food consumption of U.S. adults: impact on energy and nutrient intakes and overweight status . J Am Coll Nutr. 2004 ; 23 : 163 – 168 .

Duffey KJ Gordon-Larsen P Jacobs DR Jr , et al. Differential associations of fast food and restaurant food consumption with 3-y change in body mass index. The Coronary Artery Risk Development in Young Adults Study . Am J Clin Nutr. 2007 ; 85 : 201 – 208 .

Krishnan S Coogan PF Boggs DA , et al. Consumption of restaurant foods and incidence of type 2 diabetes in African American women . Am J Clin Nutr. 2010 ; 91 : 465 – 471 .

Duffey KJ Gordon-Larsen P Steffen LM , et al. Regular consumption from fast food establishments relative to other restaurants is differentially associated with metabolic outcomes in young adults . J Nutr. 2009 ; 139 : 2113 – 2118 .

Clemens LH Slawson DL Klesges RC . The effect of eating out on quality of diet in premenopausal women . J Am Diet Assoc. 1999 ; 99 : 442 – 444 .

Bowman SA Gortmaker SL Ebbeling CB , et al. Effects of fast-food consumption on energy intake and diet quality among children in a national household survey . Pediatrics. 2004 ; 113 : 112 – 118 .

Rolls BJ Bell EA Castellanos VH , et al. Energy density but not fat content of foods affected energy intake in lean and obese women . Am J Clin Nutr. 1999 ; 69 : 863 – 871 .

Bell EA Castellanos VH Pelkman CL , et al. Energy density of foods affects energy intake in normal-weight women . Am J Clin Nutr. 1998 ; 67 : 412 – 420 .

Rolls BJ . The role of energy density in the overconsumption of fat . J Nutr. 2000 ; 130 ( 2S Suppl ): S268 – S271 .

Seagle H Davy B Grunwald G , et al. Energy density of self-reported food intake: variation and relationship to other food components [Abstract] . Obes Res. 1997 ; 5 : 78S .

Poppitt SD Prentice AM . Energy density and its role in the control of food intake: evidence from metabolic and community studies . Appetite. 1996 ; 26 : 153 – 174 .

Stubbs RJ Harbron CG Prentice AM . Covert manipulation of the dietary fat to carbohydrate ratio of isoenergetically dense diets: effect on food intake in feeding men ad libitum . Int J Obes. 1996 ; 20 : 651 – 660 .

Saltzman E Dallal GE Roberts SB . Effect of high-fat and low-fat diets on voluntary energy intake and substrate oxidation: studies in identical twins consuming diets matched for energy density, fiber and palatability . Am J Clin Nutr. 1997 ; 66 : 1332 – 1339 .

Burns C Jackson M Gibbons C , et al. Foods prepared outside the home: association with selected nutrients and body mass index in adult Australians . Public Health Nutr. 2002 ; 5 : 441 – 448 .

Schröder H Fïto M Covas MI . Association of fast food consumption with energy intake, diet quality, body mass index and the risk of obesity in a representative Mediterranean population . Br J Nutr. 2007 ; 98 : 1274 – 1280 .

Kjøllesdal MR Holmboe-Ottesen G Wandel M . Frequent use of staff canteens is associated with unhealthy dietary habits and obesity in a Norwegian adult population . Public Health Nutr. 2010 ; 14 : 133 – 141 .

Rouhani MH Mirseifinezhad M Omrani N , et al. Fast food consumption, quality of diet, and obesity among Isfahanian adolescent girls . J Obes. 2012 ; No. 2012 : doi: 10.1155/2012/597924 .

French SA Harnack L Jeffery RW . Fast food restaurant use among women in the Pound of Prevention study: dietary, behavioral and demographic correlates . Int J Obes. 2000 ; 24 : 1353 – 1359 .

Christiansen E Garby L Sorensen TI . Quantitative analysis of the energy requirements for development of obesity . J Theor Biol. 2005 ; 234 : 99 – 106 .

Ball K Brown W Crawford D . Who does not gain weight? Prevalence and predictors of weight maintenance in young women . Int J Obes. 2002 ; 26 : 1570 – 1578 .

Prentice AM Jebb SA . Fast foods, energy density and obesity: a possible mechanistic link . Obes Rev. 2003 ; 4 : 187 – 194 .

Isganaitis E Lustig RH . Fast food, central nervous system insulin resistance, and obesity . Arterioscler Thromb Vasc Biol. 2005 ; 25 : 2451 – 2462 .

Bauer KW Hearst MO Earnest AA , et al. Energy content of U.S. fast-food restaurant offerings: 14-year trends . Am J Prev Med. 2012 ; 43 : 490 – 497 .

Mancino L Todd J Lin BH . Separating what we eat from where: measuring the effect of food away from home on diet quality . Food Policy. 2009 ; 34 : 557 – 562 .

Alfieri M Pomerleau J Grace DM . A comparison of fat intake of normal weight, moderately obese and severely obese subjects . Obes Surg. 1997 ; 7 : 9 – 15 .

Satia-Abouta J Patterson RE Schiller RN , et al. Energy from fat is associated with obesity in U.S. men: results from the Prostate Cancer Prevention Trial . Prev Med. 2002 ; 34 : 493 – 501 .

Larson DE Hunter GR Williams MJ , et al. Dietary fat in relation to body fat and intraabdominal adipose tissue: a cross-sectional analysis . Am J Clin Nutr. 1996 ; 64 : 677 – 684 .

Cotton JR Burley VJ Weststrate JA , et al. Dietary fat and appetite: similarities and differences in the satiating effect of meals supplemented with either fat or carbohydrate . J Hum Nutr Diet. 2007 ; 20 : 186 – 199 .

Bennett C Reed GW Peters JC , et al. Short-term effects of dietary-fat ingestion on energy expenditure and nutrient balance . Am J Clin Nutr. 1992 ; 55 : 1071 – 1077 .

Horton TJ Drougas H Brachey A , et al. Fat and carbohydrate overfeeding in humans: different effects on energy storage . Am J Clin Nutr. 1995 ; 62 : 19 – 29 .

Raben A Agerholm-Larsen L Flint A , et al. Meals with similar energy densities but rich in protein, fat, carbohydrate, or alcohol have different effects on energy expenditure and substrate metabolism but not on appetite and energy intake . Am J Clin Nutr. 2003 ; 77 : 91 – 100 .

Griffiths AJ Humphreys SM Clark ML , et al. Immediate metabolic availability of dietary fat in combination with carbohydrate . Am J Clin Nutr. 1994 ; 59 : 53 – 59 .

Lammert O Grunnet N Faber P , et al. Effects of isoenergetic overfeeding of either carbohydrate or fat in young men . Br J Nutr. 2000 ; 84 : 233 – 245 .

Warwick ZS Schiffman SS . Role of dietary fat in calorie intake and weight gain . Neurosci Biobehav Rev. 1992 ; 16 : 585 – 596 .

Zandstra EH De Graaf C Meal DJ , et al. Short- and long-term effects of changes in pleasantness on food intake . Appetite. 2000 ; 34 : 253 – 260 .

De Castro JM Bellisle F Dalix A , et al. Palatability and intake relationship in free-living humans: characterization and independence of influence in North Americans . Physiol Behav. 2000 ; 70 : 343 – 350 .

De Castro JM Bellisle F Dalix A . Palatability and intake relationships in free-living humans: measurement and characterization in the French . Physiol Behav. 2000 ; 68 : 271 – 277 .

Yeomans MR Gray RW Mitchell CJ , et al. Independent effects of palatability and within-meal pauses on intake and appetite ratings in human volunteers . Appetite. 1997 ; 29 : 61 – 76 .

Sørensen LB Møller P Flint A , et al. Effect of sensory perception of foods on appetite and food intake: a review of studies on humans . Int J Obes. 2003 ; 27 : 1152 – 1166 .

Hunter JE Zhang J Kris-Etherton PM . Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review . Am J Clin Nutr. 2010 ; 91 : 46 – 63 .

Mensink RP Zock PL Kester AD , et al. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials . Am J Clin Nutr. 2003 ; 77 : 1146 – 1155 .

Parodi PW . Has the association between saturated fatty acids, serum cholesterol and coronary heart disease been over emphasized? Int Dairy J. 2009 ; 19 : 345 – 361 .

Astrup A Dyerberg J Elwood P , et al. The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? Am J Clin Nutr. 2011 ; 93 : 684 – 688 .

Risérus U Willett WC Hu FB . Dietary fats and prevention of type 2 diabetes . Prog Lipid Res. 2009 ; 48 : 44 – 51 .

Thanopoulou AC Karamanos BG Angelico FV , et al. Dietary fat intake as risk factor for the development of diabetes: multinational, multicenter study of the Mediterranean Group for the Study of Diabetes (MGSD) . Diabetes Care. 2003 ; 26 : 302 – 307 .

Wang L Folsom AR Zheng ZJ , et al. Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) study . Am J Clin Nutr. 2003 ; 78 : 91 – 98 .

Siri-Tarino PW Sun Q Hu FB , et al. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease . Am J Clin Nutr. 2010 ; 91 : 535 – 546 .

Kurahashi N Inoue M Iwasaki M , et al. Dairy product, saturated fatty acid, and calcium intake and prostate cancer in a prospective cohort of Japanese men . Cancer Epidemiol Biomarkers Prev. 2008 ; 17 : 930 – 937 .

Gerber M . Background review paper on total fat, fatty acid intake and cancers . Ann Nutr Metab. 2009 ; 55 : 140 – 161 .

Matthäus B . Use of palm oil for frying in comparison with other high-stability oils . Eur J Lipid Sci Technol. 2007 ; 109 : 400 – 409 .

Berger KG . The use of palm oil in frying . In: Frying Oil Series . Selangor, Malaysia ; Malaysian Palm Oil Promotion Council. Available at: http://www.americanpalmoil.com/publications/Palm%20Oil%20in%20Frying.pdf . Accessed 22 March 2013 .

Vega-López S Ausman LM Jalbert SM , et al. Palm and partially hydrogenated soybean oils adversely alter lipoprotein profiles compared with soybean and canola oils in moderately hyperlipidemic subjects . Am J Clin Nutr. 2006 ; 84 : 54 – 62 .

Vessby B Uusitupa M Hermansen K , et al. Substituting dietary saturated for monounsaturated fat impairs insulin sensitivity in healthy men and women: the KANWU Study . Diabetologia. 2001 ; 44 : 312 – 319 .

Stender S Dyerberg J Astrup A . Fast food: unfriendly and unhealthy . Int J Obes. 2007 ; 31 : 887 – 890 .

Dunford E Webster J Barzi F , et al. Nutrient content of products served by leading Australian fast food chains . Appetite. 2010 ; 55 : 484 – 489 .

Mendis S Cruz-Hernandez C Ratnayake WM . Fatty acid profile of Canadian dairy products with special attention to the trans-octadecenoic acid and conjugated linoleic acid isomers . J AOAC Int. 2008 ; 91 : 811 – 819 .

Motard-Bélanger A Charest A Grenier G , et al. Study of the effect of trans fatty acids from ruminants on blood lipids and other risk factors for cardiovascular disease . Am J Clin Nutr. 2008 ; 87 : 593 – 599 .

Hulshof KF van Erp-Baart MA Anttolainen M , et al. Intake of fatty acids in Western Europe with emphasis on trans fatty acids: the TRANSFAIR Study . Eur J Clin Nutr. 1999 ; 53 : 143 – 157 .

Doell D Folmer D Lee H , et al. Updated estimate of trans fat intake by the US population . Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2012 ; 29 : 861 – 874 .

Kris-Etherton PM Lefevre M Mensink RP , et al. Trans fatty acid intakes and food sources in the U.S. population: NHANES 1999–2002 . Lipids. 2012 ; 47 : 931 – 940 .

Pot GK Prynne CJ Roberts C , et al. National Diet and Nutrition Survey: fat and fatty acid intake from the first year of the rolling programme and comparison with previous surveys . Br J Nutr. 2012 ; 107 : 405 – 415 .

Teegala SM Willett WC Mozaffarian D . Consumption and health effects of trans fatty acids: a review . J AOAC Int. 2009 ; 92 : 1250 – 1257 .

Mozaffarian D Aro A Willett WC . Health effects of trans-fatty acids: experimental and observational evidence . Eur J Clin Nutr. 2009 ; 63 ( Suppl 2 ): S5 – S21 .

Mozaffarian D Clarke R . Quantitative effects on cardiovascular risk factors and coronary heart disease risk of replacing partially hydrogenated vegetable oils with other fats and oils . Eur J Clin Nutr. 2009 ; 63 ( Suppl 2 ): S22 – S33 .

Esmaillzadeh A Azadbakht L . Home use of vegetable oils, markers of systemic inflammation, and endothelial dysfunction among women . Am J Clin Nutr. 2008 ; 88 : 913 – 921 .

Lopez-Garcia E Schulze MB Meigs JB , et al. Consumption of trans fatty acids is related to plasma biomarkers of inflammation and endothelial dysfunction . J Nutr. 2005 ; 135 : 562 – 566 .

Ganguly R Pierce GN . Trans fat involvement in cardiovascular disease . Mol Nutr Food Res. 2012 ; 56 : 1090 – 1096 .

Micha R Mozaffarian D . Trans fatty acids: effects on cardiometabolic health and implications for policy . Prostaglandins Leukot Essent Fatty Acids. 2008 ; 79 : 147 – 152 .

Laake I Pedersen JI Selmer R , et al. A prospective study of intake of trans-fatty acids from ruminant fat, partially hydrogenated vegetable oils, and marine oils and mortality from CVD . Br J Nutr. 2012 ; 108 : 743 – 754 .

Koh-Banerjee P Chu NF Spiegelman D , et al. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men . Am J Clin Nutr. 2003 ; 78 : 719 – 727 .

Mojska H . Influence of trans fatty acids on infant and fetus development . Acta Microbiol Pol. 2003 ; 52 ( Suppl ): 67 – 74 .

Mojska H Socha P Socha J , et al. Trans fatty acids in human milk in Poland and their association with breastfeeding mothers' diets . Acta Paediatr. 2003 ; 92 : 1381 – 1387 .

Anderson AK McDougald DM Steiner-Asiedu M . Dietary trans fatty acid intake and maternal and infant adiposity . Eur J Clin Nutr. 2010 ; 64 : 1308 – 1315 .

Wagner KH Plasser E Proell C , et al. Comprehensive studies on the trans fatty acid content of Austrian foods: convenience products, fast food and fats . Food Chem. 2008 ; 108 : 1054 – 1060 .

New South Wales Food Authority . Report on 2009 Trans Fatty Acid Survey – Analytical Results . A survey conducted under the Coordinated Food Survey Plan with participation from food regulatory jurisdiction in NSW, SA, WA and NZ. Food Standards Australia New Zealand, 2009 ; Available at: http://www.foodstandards.gov.au/_srcfiles/TFA_survey_analytical_2009.pdf . Accessed 19 March 2013.

Tyburczy C Delmonte P Fardin-Kia AR , et al. Profile of trans fatty acids (FAs) including trans polyunsaturated FAs in representative fast food samples . J Agric Food Chem. 2012 ; 60 : 4567 – 4577 .

Department of Health . Dietary Reference Values for Food Energy and Nutrients for the United Kingdom . Report of the Panel on DRVs of the Committee on Medical Aspects of Food Policy (COMA), Report of Health and Social Subjects No 41. London: The Stationary Office; 1991 .

National Institute for Health and Clinical Excellence . The Prevention of Cardiovascular Disease at a Population Level . 2010 ; Available at: http://www.nice.org.uk/nicemedia/pdf/CVDEP9TransFats.pdf . Accessed 19 March 2013.

Lichtenstein AH . New York City trans fat ban: improving the default option when purchasing foods prepared outside of the home . Ann Intern Med. 2012 ; 157 : 144 – 145 .

Angell SY Cobb LK Curtis CJ , et al. Change in trans fatty acid content of fast-food purchases associated with New York City's restaurant regulation: a pre-post study . Ann Intern Med. 2012 ; 157 : 81 – 86 .

Jaworowska A Blackham T Stevenson L . Nutritional composition of takeaway meals served by independent small outlets. [Abstract] . Proc Nutr Soc. 2011 ; 70 ( OCE4 ): E166 .

Jaworowska A Blackham T Stevenson L , et al. Determination of salt content in hot takeaway meals in the United Kingdom . Appetite. 2012 ; 59 : 517 – 522 .

Blackham T Abayomi JC Davies IG . Comparison of the nutritional quality of Indian takeaway and supermarket ready meals. [Abstract] . Proc Nutr Soc. 2012 ; 71 ( OCE2 ): E159 .

Stevenson L Jaworowska A Blackham T . Comparison of the nutritional quality of takeaway and ready to eat meals. [Abstract] . Proc Nutr Soc. 2011 ; 70 ( OCE4 ): E160 .

Pietinen P Valsta LM Hirvonen T , et al. Labelling the salt content in foods: a useful tool in reducing sodium intake in Finland . Public Health Nutr. 2007 ; 11 : 335 – 340 .

Leth T Jensen HG Mikkelsen AA , et al. The effect of the regulation on trans fatty acid content in Danish food . Atheroscler Suppl. 2006 ; 7 : 53 – 56 .

• trans fatty acids

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1753-4887
• Print ISSN 0029-6643
• Copyright © 2024 International Life Sciences Institute
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Nutritional Medicine
• Food Science
• Nutrition and Dietetics

Fast foods and their impact on health

• January 2012
• Journal of Krishna Institute of Medical Sciences University 1(2):7-15
• This person is not on ResearchGate, or hasn't claimed this research yet.

• ESI-MC&PGIMSR, Rajajinagar, Bangalore, India.

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Yumna Mussaddiq

• Gulab Shahzadi
• Priya Keshari
• Deeksha Giri
• Karina Araceli Calderón Valarezo
• Kelly Marita Solis Cullo

• Neeru S Juneja
• Bontha V. Babu

• Asmaa Gomaa Mohammed
• Amel Abd-Elazim Mohamed
• Mervat Abd Elkader Ahmed

• Akanksha Baskati
• Dr. Navita Pareek
• Vinita Tomer
• Sonika Chaudhary
• CYBERPSYCHOL BEHAV

• J Pakistan Med Assoc

• Majid Hafeez Qadri

• Shakeela Perveen

• Raymond Macharia
• Samantha J Farrington
• N C Stickland
• Nighat Nisar
• J PUBLIC HEALTH-UK

• Roland Sturm

• J Toxicol Sci
• Keisuke Nakayama

• Sadayoshi Ito

• A Antoniciello
• Sharron Dalton
• Shu-ping Zhu
• Yue-jiang Ding
• Xiang-Feng Lu
• PUBLIC HEALTH NUTR

• Kostas Mihas

• J AM DIET ASSOC

• Susan M Krebs-Smith
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

A systematic review of fast food access studies

Affiliation.

• 1 Department of Nutrition in the Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA. [email protected]
• PMID: 20149118
• DOI: 10.1111/j.1467-789X.2010.00715.x

The frequent consumption of energy-dense fast food is associated with increased body mass index. This systematic review aims to examine the methodology and current evidence on fast food access and its associations with outcomes. Six databases were searched using terms relating to fast food. Only peer-reviewed studies published in English during a 10-year period, with data collection and analysis regarding fast food access were included. Forty articles met the aforementioned criteria. Nearly half of the studies (n = 16) used their own set of features to define fast food. Studies predominantly examined the relationship between fast food access and socioeconomic factors (n = 21) and 76% indicated fast food restaurants were more prevalent in low-income areas compared with middle- to higher-income areas. Ten of 12 studies found fast food restaurants were more prevalent in areas with higher concentrations of ethnic minority groups in comparison with Caucasians. Six adult studies found higher body mass index was associated with living in areas with increased exposure to fast food; four studies, however, did not find associations. Further work is needed to understand if and how fast food access impacts dietary intake and health outcomes; and if fast food access has disparate socioeconomic, race/ethnicity and age associations.

© 2010 The Authors. obesity reviews © 2010 International Association for the Study of Obesity.

PubMed Disclaimer

Similar articles

• Sense of mastery and metabolic risk: moderating role of the local fast-food environment. Paquet C, Dubé L, Gauvin L, Kestens Y, Daniel M. Paquet C, et al. Psychosom Med. 2010 Apr;72(3):324-31. doi: 10.1097/PSY.0b013e3181cdf439. Epub 2010 Jan 25. Psychosom Med. 2010. PMID: 20100887
• Absolute and relative densities of fast-food versus other restaurants in relation to weight status: Does restaurant mix matter? Polsky JY, Moineddin R, Dunn JR, Glazier RH, Booth GL. Polsky JY, et al. Prev Med. 2016 Jan;82:28-34. doi: 10.1016/j.ypmed.2015.11.008. Epub 2015 Nov 12. Prev Med. 2016. PMID: 26582211
• Impact of Social and Built Environment Factors on Body Size among Breast Cancer Survivors: The Pathways Study. Shariff-Marco S, Von Behren J, Reynolds P, Keegan TH, Hertz A, Kwan ML, Roh JM, Thomsen C, Kroenke CH, Ambrosone C, Kushi LH, Gomez SL. Shariff-Marco S, et al. Cancer Epidemiol Biomarkers Prev. 2017 Apr;26(4):505-515. doi: 10.1158/1055-9965.EPI-16-0932. Epub 2017 Feb 2. Cancer Epidemiol Biomarkers Prev. 2017. PMID: 28154107 Free PMC article.
• Eating out of home and its association with dietary intake: a systematic review of the evidence. Lachat C, Nago E, Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P. Lachat C, et al. Obes Rev. 2012 Apr;13(4):329-46. doi: 10.1111/j.1467-789X.2011.00953.x. Epub 2011 Nov 23. Obes Rev. 2012. PMID: 22106948 Review.
• A Review of the Growth of the Fast Food Industry in China and Its Potential Impact on Obesity. Wang Y, Wang L, Xue H, Qu W. Wang Y, et al. Int J Environ Res Public Health. 2016 Nov 9;13(11):1112. doi: 10.3390/ijerph13111112. Int J Environ Res Public Health. 2016. PMID: 27834887 Free PMC article. Review.
• Association of Body Mass Index with Moderate-Intensity Physical Activity, Eating Behavior, and the Use of the Internet or Games among Korean Middle School Students. Kwon J, Roh SY, Kwon D. Kwon J, et al. Children (Basel). 2024 Aug 16;11(8):1000. doi: 10.3390/children11081000. Children (Basel). 2024. PMID: 39201936 Free PMC article.
• Young people's views and experience of diet-related inequalities in England (UK): a qualitative study. Er V, Crowder M, Holding E, Woodrow N, Griffin N, Summerbell C, Egan M, Fairbrother H. Er V, et al. Health Promot Int. 2024 Aug 1;39(4):daae107. doi: 10.1093/heapro/daae107. Health Promot Int. 2024. PMID: 39175414 Free PMC article.
• Effect of mobile food environments on fast food visits. García Bulle Bueno B, Horn AL, Bell BM, Bahrami M, Bozkaya B, Pentland A, de la Haye K, Moro E. García Bulle Bueno B, et al. Nat Commun. 2024 Mar 14;15(1):2291. doi: 10.1038/s41467-024-46425-2. Nat Commun. 2024. PMID: 38480685 Free PMC article.
• [Research on Healthy Neighborhood Evaluation System Based on the Combined Perspectives of Urban Planning and Public Health]. 雨洋 张, 宁睿 刘, 瀛 龙, Yuyang Z, Ningrui L, Ying L. 雨洋 张, et al. Feng Jing Yuan Lin. 2020;27(11):96-103. doi: 10.14085/j.fjyl.2020.11.0096.08. Feng Jing Yuan Lin. 2020. PMID: 38420146 Free PMC article. Chinese.
• Lack of food access and food consumption patterns of late midlife women in southeast Michigan. Bassiouni SS, White EM, Karvonen-Gutierrez C, Harlow SD. Bassiouni SS, et al. Mich J Public Health. 2020 Feb;10(1):8. Mich J Public Health. 2020. PMID: 38274401 Free PMC article. No abstract available.

Publication types

• Search in MeSH

Related information

• Cited in Books

Grants and funding

• T 32 MH75854/MH/NIMH NIH HHS/United States

LinkOut - more resources

Full text sources.

• MedlinePlus Health Information

Miscellaneous

• NCI CPTAC Assay Portal

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Frontiers for Young Minds

• Download PDF

The Impacts of Junk Food on Health

Energy-dense, nutrient-poor foods, otherwise known as junk foods, have never been more accessible and available. Young people are bombarded with unhealthy junk-food choices daily, and this can lead to life-long dietary habits that are difficult to undo. In this article, we explore the scientific evidence behind both the short-term and long-term impacts of junk food consumption on our health.

Introduction

The world is currently facing an obesity epidemic, which puts people at risk for chronic diseases like heart disease and diabetes. Junk food can contribute to obesity and yet it is becoming a part of our everyday lives because of our fast-paced lifestyles. Life can be jam-packed when you are juggling school, sport, and hanging with friends and family! Junk food companies make food convenient, tasty, and affordable, so it has largely replaced preparing and eating healthy homemade meals. Junk foods include foods like burgers, fried chicken, and pizza from fast-food restaurants, as well as packaged foods like chips, biscuits, and ice-cream, sugar-sweetened beverages like soda, fatty meats like bacon, sugary cereals, and frozen ready meals like lasagne. These are typically highly processed foods , meaning several steps were involved in making the food, with a focus on making them tasty and thus easy to overeat. Unfortunately, junk foods provide lots of calories and energy, but little of the vital nutrients our bodies need to grow and be healthy, like proteins, vitamins, minerals, and fiber. Australian teenagers aged 14–18 years get more than 40% of their daily energy from these types of foods, which is concerning [ 1 ]. Junk foods are also known as discretionary foods , which means they are “not needed to meet nutrient requirements and do not belong to the five food groups” [ 2 ]. According to the dietary guidelines of Australian and many other countries, these five food groups are grains and cereals, vegetables and legumes, fruits, dairy and dairy alternatives, and meat and meat alternatives.

Young people are often the targets of sneaky advertising tactics by junk food companies, which show our heroes and icons promoting junk foods. In Australia, cricket, one of our favorite sports, is sponsored by a big fast-food brand. Elite athletes like cricket players are not fuelling their bodies with fried chicken, burgers, and fries! A study showed that adolescents aged 12–17 years view over 14.4 million food advertisements in a single year on popular websites, with cakes, cookies, and ice cream being the most frequently advertised products [ 3 ]. Another study examining YouTube videos popular amongst children reported that 38% of all ads involved a food or beverage and 56% of those food ads were for junk foods [ 4 ].

What Happens to Our Bodies Shortly After We Eat Junk Foods?

Food is made up of three major nutrients: carbohydrates, proteins, and fats. There are also vitamins and minerals in food that support good health, growth, and development. Getting the proper nutrition is very important during our teenage years. However, when we eat junk foods, we are consuming high amounts of carbohydrates, proteins, and fats, which are quickly absorbed by the body.

Let us take the example of eating a hamburger. A burger typically contains carbohydrates from the bun, proteins and fats from the beef patty, and fats from the cheese and sauce. On average, a burger from a fast-food chain contains 36–40% of your daily energy needs and this does not account for any chips or drinks consumed with it ( Figure 1 ). This is a large amount of food for the body to digest—not good if you are about to hit the cricket pitch!

• Figure 1 - The nutritional composition of a popular burger from a famous fast-food restaurant, detailing the average quantity per serving and per 100 g.
• The carbohydrates of a burger are mainly from the bun, while the protein comes from the beef patty. Large amounts of fat come from the cheese and sauce. Based on the Australian dietary guidelines, just one burger can be 36% of the recommended daily energy intake for teenage boys aged 12–15 years and 40% of the recommendations for teenage girls 12–15 years.

A few hours to a few days after eating rich, heavy foods such as a burger, unpleasant symptoms like tiredness, poor sleep, and even hunger can result ( Figure 2 ). Rather than providing an energy boost, junk foods can lead to a lack of energy. For a short time, sugar (a type of carbohydrate) makes people feel energized, happy, and upbeat as it is used by the body for energy. However, refined sugar , which is the type of sugar commonly found in junk foods, leads to a quick drop in blood sugar levels because it is digested quickly by the body. This can lead tiredness and cravings [ 5 ].

• Figure 2 - The short- and long-term impacts of junk food consumption.
• In the short-term, junk foods can make you feel tired, bloated, and unable to concentrate. Long-term, junk foods can lead to tooth decay and poor bowel habits. Junk foods can also lead to obesity and associated diseases such as heart disease. When junk foods are regularly consumed over long periods of time, the damages and complications to health are increasingly costly.

Fiber is a good carbohydrate commonly found in vegetables, fruits, barley, legumes, nuts, and seeds—foods from the five food groups. Fiber not only keeps the digestive system healthy, but also slows the stomach’s emptying process, keeping us feeling full for longer. Junk foods tend to lack fiber, so when we eat them, we notice decreasing energy and increasing hunger sooner.

Foods such as walnuts, berries, tuna, and green veggies can boost concentration levels. This is particularly important for young minds who are doing lots of schoolwork. These foods are what most elite athletes are eating! On the other hand, eating junk foods can lead to poor concentration. Eating junk foods can lead to swelling in the part of the brain that has a major role in memory. A study performed in humans showed that eating an unhealthy breakfast high in fat and sugar for 4 days in a row caused disruptions to the learning and memory parts of the brain [ 6 ].

Long-Term Impacts of Junk Foods

If we eat mostly junk foods over many weeks, months, or years, there can be several long-term impacts on health ( Figure 2 ). For example, high saturated fat intake is strongly linked with high levels of bad cholesterol in the blood, which can be a sign of heart disease. Respected research studies found that young people who eat only small amounts of saturated fat have lower total cholesterol levels [ 7 ].

Frequent consumption of junk foods can also increase the risk of diseases such as hypertension and stroke. Hypertension is also known as high blood pressure and a stroke is damage to the brain from reduced blood supply, which prevents the brain from receiving the oxygen and nutrients it needs to survive. Hypertension and stroke can occur because of the high amounts of cholesterol and salt in junk foods.

Furthermore, junk foods can trigger the “happy hormone,” dopamine , to be released in the brain, making us feel good when we eat these foods. This can lead us to wanting more junk food to get that same happy feeling again [ 8 ]. Other long-term effects of eating too much junk food include tooth decay and constipation. Soft drinks, for instance, can cause tooth decay due to high amounts of sugar and acid that can wear down the protective tooth enamel. Junk foods are typically low in fiber too, which has negative consequences for gut health in the long term. Fiber forms the bulk of our poop and without it, it can be hard to poop!

Tips for Being Healthy

One way to figure out whether a food is a junk food is to think about how processed it is. When we think of foods in their whole and original forms, like a fresh tomato, a grain of rice, or milk squeezed from a cow, we can then start to imagine how many steps are involved to transform that whole food into something that is ready-to-eat, tasty, convenient, and has a long shelf life.

For teenagers 13–14 years old, the recommended daily energy intake is 8,200–9,900 kJ/day or 1,960 kcal-2,370 kcal/day for boys and 7,400–8,200 kJ/day or 1,770–1,960 kcal for girls, according to the Australian dietary guidelines. Of course, the more physically active you are, the higher your energy needs. Remember that junk foods are okay to eat occasionally, but they should not make up more than 10% of your daily energy intake. In a day, this may be a simple treat such as a small muffin or a few squares of chocolate. On a weekly basis, this might mean no more than two fast-food meals per week. The remaining 90% of food eaten should be from the five food groups.

In conclusion, we know that junk foods are tasty, affordable, and convenient. This makes it hard to limit the amount of junk food we eat. However, if junk foods become a staple of our diets, there can be negative impacts on our health. We should aim for high-fiber foods such as whole grains, vegetables, and fruits; meals that have moderate amounts of sugar and salt; and calcium-rich and iron-rich foods. Healthy foods help to build strong bodies and brains. Limiting junk food intake can happen on an individual level, based on our food choices, or through government policies and health-promotion strategies. We need governments to stop junk food companies from advertising to young people, and we need their help to replace junk food restaurants with more healthy options. Researchers can focus on education and health promotion around healthy food options and can work with young people to develop solutions. If we all work together, we can help young people across the world to make food choices that will improve their short and long-term health.

Obesity : ↑ A disorder where too much body fat increases the risk of health problems.

Processed Food : ↑ A raw agricultural food that has undergone processes to be washed, ground, cleaned and/or cooked further.

Discretionary Food : ↑ Foods and drinks not necessary to provide the nutrients the body needs but that may add variety to a person’s diet (according to the Australian dietary guidelines).

Refined Sugar : ↑ Sugar that has been processed from raw sources such as sugar cane, sugar beets or corn.

Saturated Fat : ↑ A type of fat commonly eaten from animal sources such as beef, chicken and pork, which typically promotes the production of “bad” cholesterol in the body.

Dopamine : ↑ A hormone that is released when the brain is expecting a reward and is associated with activities that generate pleasure, such as eating or shopping.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

[1] ↑ Australian Bureau of Statistics. 2013. 4324.0.55.002 - Microdata: Australian Health Survey: Nutrition and Physical Activity, 2011-12 . Australian Bureau of Statistics. Available online at: http://bit.ly/2jkRRZO (accessed December 13, 2019).

[2] ↑ National Health and Medical Research Council. 2013. Australian Dietary Guidelines Summary . Canberra, ACT: National Health and Medical Research Council.

[3] ↑ Potvin Kent, M., and Pauzé, E. 2018. The frequency and healthfulness of food and beverages advertised on adolescents’ preferred web sites in Canada. J. Adolesc. Health. 63:102–7. doi: 10.1016/j.jadohealth.2018.01.007

[4] ↑ Tan, L., Ng, S. H., Omar, A., and Karupaiah, T. 2018. What’s on YouTube? A case study on food and beverage advertising in videos targeted at children on social media. Child Obes. 14:280–90. doi: 10.1089/chi.2018.0037

[5] ↑ Gómez-Pinilla, F. 2008. Brain foods: the effects of nutrients on brain function. Nat. Rev. Neurosci. 9, 568–78. doi: 10.1038/nrn2421

[6] ↑ Attuquayefio, T., Stevenson, R. J., Oaten, M. J., and Francis, H. M. 2017. A four-day western-style dietary intervention causes reductions in hippocampal-dependent learning and memory and interoceptive sensitivity. PLoS ONE . 12:e0172645. doi: 10.1371/journal.pone.0172645

[7] ↑ Te Morenga, L., and Montez, J. 2017. Health effects of saturated and trans-fatty acid intake in children and adolescents: systematic review and meta-analysis. PLoS ONE. 12:e0186672. doi: 10.1371/journal.pone.0186672

[8] ↑ Reichelt, A. C. 2016. Adolescent maturational transitions in the prefrontal cortex and dopamine signaling as a risk factor for the development of obesity and high fat/high sugar diet induced cognitive deficits. Front. Behav. Neurosci. 10. doi: 10.3389/fnbeh.2016.00189

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Prev Med Hyg
• v.59(3); 2018 Sep

Fast food consumption and overweight/obesity prevalence in students and its association with general and abdominal obesity

A. mohammadbeigi.

1 Research Center of Gastroenterology and Hepatology, Qom University of Medical Sciences, Qom, Iran

A. ASGARIAN

2 Research Center for Air Pollutants, Qom University of Medical Sciences, Qom/Iran

3 Department of Anesthesiology, Faculty of Medicine, Arak University of Medical Sciences, Arak, Iran

S. AFRASHTEH

4 Department of Public Health, Vice chancellor of Health, MSc of Epidemiology, Bushehr University of Medical Sciences, Bushehr, Iran

5 Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

6 Health Promotion Research Center, Department of Epidemiology and Biostatistics, Zahedan University of Medical Sciences, Zahedan, Iran

Nowadays, the prevalence of both fast food consumption and overweight/obesity has been increased. This study aimed to estimate the prevalence of fast food consumption and to assess its association with abdominal and general obesity. In an analytical cross-sectional study, 300 students were selected randomly from two largest universities in Qom, center of Iran, studying in medical and basic sciences fields in 2015. Data collection was conducted by a modified version of NELSON’s fast food questionnaire and anthropometric measures including Waist-Hip Ratio (WHR) and Body Mass Index (BMI). Chi-square, independent t-test, and multivariate logistic regression were used for statistical analysis. According to our results, 72.4% (67.4% in females vs 80.7% in males) had at least one type of fast food consumption in the recent month including sandwich 44.4%, pizza 39.7%, and fried chicken 13.8%, The obesity prevalence based on BMI and WHR was 21.3% (95% CI: 19.4, 23.2%) and 33.2% (95% CI: 0.7, 35.7), respectively. Fast food consumption was related to abdominal obesity as WHR (OR: 1.46, 95% CI: 1.11, 2.26), but was not related to general obesity as BMI (OR: 0.97, 95% CI: 0.63, 1.52). The prevalence of fast food consumption and obesity/overweight in Iranian student is high. Fast food consumption was associated with abdominal obesity based WHR, but did not related to general obesity based on BMI.

In adolescent students, 72.4% and 34% have used at least one type of fast foods in recent month and in recent week.

The obesity prevalence based on BMI and WHR was 21.3 % (18.2% in females vs 26.3% in males) and 33.2% (40.1% in females vs 21.9% in males), respectively.

Fast food consumption was associated with WHR, while was not related to BMI.

Sandwich consumption was associated with obesity/overweight based on BMI to 35%, fried chicken to 40%, and pizza more than 80%.

Introduction

The percentage of caloric intake from fast foods has increased fivefold over the past three decades among adolescents [ 1 , 2 ]. In addition, obesity prevalence increased dramatically worldwide as one of the most serious public health problem especially in childhood and adolescents in current century [ 3 ]. Fast food consumption has increasing’ trend due to convenience, costs, menu choices, flavor and taste [ 4 ]. About 30% of children to more than 50% in college students use fast food daily[ 2 , 5 ]. Moreover, more than 33% of adults and 17% of children and teenagers are obese in united states [ 6 ]. Increased food consumption and substantial changes in the food habits are the most important factors of obesity epidemic [ 7 ] besides the poor diet among young people at recent years [ 8 ].

Wide ranges of causes are associated with obesity and overweight that varied from genetic to environmental factors [ 3 , 7 ]. However, our surround environment is one of the key factors that effective in the rapid development of the obesity epidemic in the world [ 7 ]. Fast food consumption is strongly associated with weight gain and obesity. Fast food consumption could increase the risk of obesity and obesity-related diseases as a major public health issue [ 9 , 10 ]. Obesity and overweight are the most important factors of non-communicable diseases related to years of life lost in cardiovascular diseases [ 11 , 12 ].

Fast food is defined by a convenience food purchased in self-service or carry out eating venues without wait service [ 9 ]. Todays, the number of women in the workforce is increased due to changes in the family structure and urbanization in all countries over the past years. Moreover, the working of people for longer hours expands and the food and mealtimes have changed seriously. A rapid growth is observed in fast food industries and restaurants [ 13 ]. Consequently, some worse consequences such as overweight and obesity have increasing trend [ 9 ]. Previous research has identified a strong positive association between the availability of fast food and its consumption as well as fast food consumption and obesity outcomes [ 5 , 8 , 10 , 14 , 15 ]. However, some studies assessed the fast food consumption on the general obesity based on Body Mass Index (BMI) [ 5 , 8 , 10 , 16 ]. Nevertheless, the association between fast food consumption and obesity type (abdominal/general) is unclear [ 3 , 10 ]. We aimed to estimate the prevalence of fast food consumption and obesity/overweight in two different governmental and nongovernmental universities, and to assess the association of fast food consumption with abdominal/general obesity.

This cross-sectional study was conducted on 300 students of two large Universities in Qom, center of Iran, that randomly selected and studying in medical and basic sciences fields at spring 2015. Sample size was calculated based on the fast food prevalence in recent studies with considering the power equal to 90% and first type error equal 5% as well as based on the minimal significant difference expected regarding fast food consumption between the two university and students who used and not used fast food. The study subjects were selected based on the multistage sampling method. In the first phase, according to the stratified random sampling method, 150 students selected from the Qom Medical University, and 150 students selected from a nongovernmental University (Qom branch of Islamic Azad University). Then in each stratum, simple random sampling was used for selecting some classes and recruitment of students. In the third phase, in each selected class, all the eligible students were called to participate in the study. After describing the objectives and the method of data gathering, the informed consent is taken from all the volunteer subjects. Moreover, the ethic committee of Qom University of Medical Sciences approve the study protocol.

Data collection was conducted by a modified version of standard NELSON’ fast food questionnaire [ 17 ]. The reliability and validity of this questionnaire is assessed by them and reported as a reliable measure with fair validity. Moreover, the content validity of modified version of questionnaire changed based on cultural and nutritional differences in Iranian people, was assessed by experts in epidemiology, nutrition and health education majors. Moreover, the reliability of questionnaire was assessed by Cronbakh Alpha and estimated as 0.861.

The main outcomes in our questionnaire were fast food consumption, type of fast food and the frequency of consumption. The variables that evaluated in fast food consumption were selected based on more frequent items that used in Iran based on cultural and religious condition such as different types of sandwich, fried chicken, fried potato, hotdog and pizza.

Obesity indexes data of such as waist and circumference for calculating Waist-Hip Ratio (WHR), height and weight for computing BMI were collected. Waist, hip circumference, and height of subjects were measured by anthropometric tape measure. Moreover, the weight of students was measured by a valid scale (SECA 830). BMI and WHR were calculated by standard formulae [ 18 , 19 ].

The WHR index was used for measuring the abdominal obesity and BMI for general obesity. Frequency, mean, and standard deviation were used for description of data. Chi-square test was used to assess the relationship between fast food consumption and quantitative demographic variables with obesity in studied subjects. Independent t-test were used for comparing the mean of age, BMI and WHR and their components in studied subjects between used and un-used fast food consumption. Finally, multivariate logistic regression was used to control the potential confounders including job, educational level, field of study and type of university. The statistical analysis was conducted using SPSS software (Chicago, IL, USA) and the type one error considered in 0.05 level.

Overall, 72.4% (67.4% in females vs 80.7% in males) have fast food consumption. These students used at least one type of the fast foods in the recent month. However, the most common type of fast food consumption was sandwich 44.4%, pizza 39.7%, fried chicken 13.8%, respectively. Figure 1 showed the distribution of different type of fast foods in recent month after survey.

The prevalence of different the types of fast food consumption in studied students.

Table I shows the comparison of fast food consumption in students by chi square test between who were consumed fast food in recent month and who not consumed. This table showed that there was significant difference between subjects who used and did not use fast food in recent month regarding to the gender, marital status, education level, university, and major of study. The married and male students as well as who studied in basic sciences and nongovernmental university were used more fast food. Nevertheless, there was no significant relationship between job and residency place at night with fast food consumption.

Table II shows that there was a significant difference between studied subjects who used and not used fast food in past month regarding to waist and WHR (p < 0.05). Nevertheless, the difference in age, weight, height, hip, and BMI was not significant between two groups.

Comparing the mean of age, BMI and WHR and their components in studied subjects between used and un-used fast food consumption.

The overweight/obesity prevalence based on BMI classification (higher 25 kg/m 2 ) was 21.3% (95% CI: 19.4, 23.2%) calculated 18.2% (95% CI: 16.1, 20.3) in females vs 26.3% (95% CI: 22.7, 29.8) in males. Moreover, the obesity prevalence based on WHR was 33.2% (95% CI: 30.7, 35.7) calculated 40.1% (95% CI: 36.6, 43.5) in females vs 21.9% (95% CI: 18.8, 25.0) in males, respectively. Therefore, we considered a subject as obese if he/she had BMI more than 25 or WHR more than 0.9 in males and more than 0.8 in females. According to this definition, 37.2% (41.2% in females vs 30.7% in males) were affected to overweight and obesity. Therefore, the consumption of fast food was related to obesity. Moreover, a significant relationship was observed between obesity and consumption of sandwich (OR: 1.35, 95% CI: 1.4, 2.41), fried chicken (OR: 1.4, 95% CI: 1.22,1.73), and pizza (OR: 1.8, 95% CI: 1.1, 2.9). In addition, the fast food consumption was related to WHR as abdominal obesity (OR: 1.46, 95 CI: 1.11, 2.26), but was not related to BMI as general obesity (OR: 0.97, 95% CI: 0.63, 1.52) ( Tab. III ). Based on multivariate regression model ( Tab. IV ) only marital status, type of university and gender were the most related factors of fast food consumption. Therefore, studying in nongovernmental university (OR: 3.16, 95% CI: 1.8, 5.6), single status (OR: 3.08, 95% CI: 1.26, 5.01) and being females (OR: 2.96, 95% CI: 1.61,4.53) are the most important related factors of fast food consumption, respectively in Qom, Iran.

The relationship between fast food consumption and obesity in studied subjects.

Multivariate analysis of predictive factors of fast food consumption in under studied subjects.

The adjusted variables in this model were job, educational level, field of study and type of university.

According to our results, 72.4% and 34% have used at least one type of the fast foods in recent month and recent week, respectively. It seems that the consumption of fast food in Qom students is high due to lack of recreational facilities and entertainment in this religious city. However, the fast food consumption in our study was lower than other studies [ 4 , 20 ]. Results of studies in students of King Faisal University reported that more than 90% of people used fast foods monthly that was higher our estimate. In addition, a same study in female students aged 18 to 25 years showed that 47.1% had fast food consumption for two or more time per week [ 5 ].

The obesity prevalence in our study was estimated 21.3% and 33.2%, based on BMI and WHR, respectively. In a previous study, the obesity/overweight prevalence was 29.7% 5 and nearly half of them used fast foods. Moreover, in Shah et al. study, more than 34% of Chinese medical students were pre-obese and obese [ 4 ].

According to our results WHR was significantly different between subjects who used and not used fast food while, the difference in BMI was not significant. Therefore, fast food consumption was related to WHR, but did not related to BMI. In addition, consumption of sandwich, fried chicken and pizza were associated with obesity/overweight based BMI. Same direct association were demonstrated the association between fast food consumption and overweight/obesity in different studies [ 10 , 14 , 15 , 21 , 22 ]. Fast foods are poor in micronutrients, low in fiber, high energy density, high in glycemic load9 and large portion size with sugar [ 4 ] and could be more energetic than the daily energy requirements [ 6 , 9 ]. In addition, the average energy density of an entire menu in fast food restaurant is approximately more than twice the energy density of a healthy menu [ 22 ]. According to some studies [ 3 , 22 , 23 ] obesity is the core of some important non-communicable diseases such as hypertension, hyperlipidemia, hypercholesterolemia, cardiovascular diseases, metabolic syndrome and type 2 diabetes [ 12 , 22 , 23 ]. Increase in energy density of diet by fat or sugar, together with concomitant eating behaviors like snacking, binge eating and eating out; promote unhealthy weight gain through passive overconsumption of energy [ 4 , 6 ].

Fast food consumption is positively related to overweight and obesity due to extremely high energy density of these foods [ 6 , 22 ]. Moreover, a study a significant association was observed between BMI and fast food consumption [ 4 ]. Two commonly eaten fast foods including fried foods and hotdogs have been associated with risk of obesity and weight gain [ 22 ]. Moreover, fast food consumption was related to general obesity in female adolescents. Moreover, obesity/overweight was significantly associated with frequency of fast food consumption [ 5 ].

This study found the prevalence of obesity was higher in females, while the prevalence of fast food consumption was higher in males. However, male students who are married are more interesting to eating fast food and it might be due to the religious culture of Qom as the most religious city of Iran. In the other hand, the single female students are not free to go in fast food restaurants than married ones. Moreover, three variables including marital status, type of university and gender are the most associated factors of fast food consumption. Based on our results in multivariate model, both studying in nongovernmental University and being single increase the odds of fast food consumption more than three fold. Moreover, female students used fast food 2.9 folds more than male students. The main reasons of students for fast food consumption are taste and comfort to access to these foods and lack of cooking skills [ 5 ]. The higher fast foods consumption in females and single students might related to lower wasting time in android social networks than male students [ 25 , 26 ]. Moreover, since in nongovernmental university the price of kitchen food is high, the students are more interesting to have eating in fast food restaurants. However, the fast food prevalence is high in students and teenagers probably due to low cost [ 4 , 16 ]. Nevertheless, because comfort accesses to fast food the corresponding expenditures are rising among people [ 15 ]. Moreover, the price of health outcomes of consequences of fast food consumption are more expensive and need to more investigations [ 9 , 15 ].

We could not measure the morphometric characters and adipocity measures of students as other body compositions indexes. Moreover, lack of cooperation of students for anthropometric measurements was another limitation of the current study.

Conclusions

The prevalence of fast food consumption and obesity/overweight in Iranian student is high. Studying in nongovernmental University, being single and females were associated with fast food consumption to three fold. Fast food consumption could have associated to abdominal obesity based WHR to 46%, but was not related to general obesity based on BMI. However, this study showed the different effect of fast foods on abdominal and general obesity as a hypothesis. Future studies need to determine the pure effect of fast food consumption on different dimensions of obesity.

The relationship between demographic variables and fast food consumption.

Acknowledgments

The authors would like to thank the research Vice-Chancellor of Qom University of Medical Sciences for financial supporting of this work. They are also grateful students who participated in this study.

Funding source: Qom University of Medical Sciences.

Conflict of interest statement

None declared.

Authors' contributions

AM: contributions to the conception, design of the work; analysis, and interpretation of data and Final approval of article.AA: contributions the acquisition and analysis of data for the work and Drafting the article. EM: contributions to the conception or design of the work; interpretation of data for the work; and Final approval of the article. SA: contributions to the conception or design of the work; interpretation of data for the work; and Final approval of the article. SK: contributions to the conception or design of the work analysis, or interpretation of data for the work; and Final approval of the article.HA: contributions to the conception, design of the work; analysis, and interpretation of data and Final approval of article.