ArticlePDF Available

Abstract

Although the associations between the consumption of alcohol, unhealthy foods, and obesity are known, there is no consensus on the mechanisms involved. Previous research demonstrates that the type of foods available during the peak times for alcohol consumption differ from those available at other times. Advertisements targeting college students indicate an awareness of increased cravings for “junk foods” following alcohol consumption, however there is no previous research on how alcohol consumption affects actual dietary choices differentially by type of food, in comparison to non-alcohol related food consumption. The current study demonstrates that college students’ food cravings increase, consumption of fruits and vegetables is lower, and consumption of junk foods is higher with alcohol consumption than at other times. Respondents reported eating something that they were craving the majority (57%) of the time; however, on average they ate something healthier than what they were craving 27% of the time. These findings help to clarify the behavioral mechanisms underlying the relationship between alcohol consumption, unhealthy dietary behaviors, and obesity.
Send correspondence to: Jessica Sloan Kruger, MSHE currently a Ph.D. Student in Health Education at the
University of Toledo, Phone +18104413881, Fax 419-530-4759 University of Toledo Mailstop 119, Oce
#1003K Toledo, Ohio 43606; Daniel J. Kruger, Ph.D. is a Research Assistant Professor in the School of
Public Health at the University of Michigan
1. INTRODUCTION
College students have a high preva-
lence of alcohol consumption (Ham & Hope,
2003; Jones, Chryssanthakis, & Groom,
2014). In 2008, 69% of college students re-
ported drinking alcohol in the past month
(Ilgen et al., 2011). Drinking alcohol can
reduce students’ inhibitions and lead them to
engage in behaviors that they may not engage in
when they are not drinking (Ilgen et al., 2011).
e “drunchies” is a portmanteau of
“drunken munchies,” a colloquial term for crav-
ings for food, especially those foods high in fat
and sodium, occurring due to alcohol consump-
tion. is term is currently used in the market-
ing of fast food to college students (Eatstreet.
com, 2014), though it has not been previously
documented in the scientic literature. Alcohol
consumption can have a signicant impact on col-
lege students’ dietary patterns, among other health
related behaviors. Eating more and/or consuming
less nutritious foods while drinking may impact
many negative health outcomes such as obesity.
College freshman who drank more alcoholic bev-
erages had a higher rst semester Body Mass Index
(BMI) (Lloyd-Richardson, Lucero, DiBello, Ja-
cobson, & Wing, 2008). Lloyd-Richardson et al.
(2008) found that students engaged in late-night
eating and experienced the “drunk munchies,
which is considered to be a disinhibition leading
to eating large quantitates of high-fat foods. is
“late night eating” is common among college stu-
dents (Lloyd-Richardson, Bailey, Fava, & Wing,
2009) and students who drank alcohol consumed
lager quantities of food and made less healthy food
choices as a result of drinking.
College students’ alcohol consumption
typically occurs during the evening and week-
ends. e highest rates of drinking are seen on
ursday, Friday, and Saturday evenings, due to
many students not having Friday classes or classes
until Monday (Wood, Sher, & Rutledge, 2007).
Because alcohol drinking begins later in the day,
food options are scarcer when it occurs. Typically,
foods considered to be unhealthy are more read-
ily available during the time most college students
would be consuming alcohol compared to health-
ier foods. Most wait service dinner restaurants
close between 9:00 and 11:00 pm, yet the food
providers with high fat, high calorie options stay
open 24 hours a day or into the early morning
hours (Nelson, Kocos, Lytle, & Perry, 2009). e
lack of access to healthy foods could explain, in
whole or part, the higher intake of high-fat foods
among college students who drink alcohol.
e nutritional literature continually de-
bates the identity of the mechanism linking alco-
hol consumption and food consumption. Previ-
ous papers have concluded that there is no clear
explanation for the short term ability for alcohol
to enhance appetite (Yeomans, 2004; Yeomans,
2010). Some hold that alcohol enhances the
short-term rewarding eects of ingestion, most
The impact of alcohol consumption on food choices among
college students
Jessica Sloan Kruger, MSHE
Daniel J. Kruger, Ph.D
Abstract: Although the associations between the consumption of alcohol, unhealthy foods, and obesity are
known, there is no consensus on the mechanisms involved. Previous research demonstrates that the type of
foods available during the peak times for alcohol consumption dier from those available at other times.
Advertisements targeting college students indicate an awareness of increased cravings for “junk foods
following alcohol consumption, however there is no previous research on how alcohol consumption aects
actual dietary choices dierentially by type of food, in comparison to non-alcohol related food consump-
tion. e current study demonstrates that college students’ food cravings increase, consumption of fruits
and vegetables is lower, and consumption of junk foods is higher with alcohol consumption than at other
times. Respondents reported eating something that they were craving the majority (57%) of the time;
however, on average they ate something healthier than what they were craving 27% of the time. ese
ndings help to clarify the behavioral mechanisms underlying the relationship between alcohol consump-
tion, unhealthy dietary behaviors, and obesity.
Keywords: Food choices, Alcohol consumption, Junk food, Drunchies
- 309 -
- 310 -
Kruger.J, Kruger D.
likely through pharmacological action (Yeomans,
2010). Others provide evidence that alcohol con-
sumption increases the inuence of implicit atti-
tudes while decreasing the inuence of reective
cognition towards food cues (Hofmann & Friese,
2008). Alcohol is a diuretic (Murray, 1932); de-
hydration associated with alcohol consumption
may induce cravings for salt in order to promote
water retention (Beauchamp, Bertino, Burke, &
Engelman, 1990). Rather than resolve the debate
on internal mechanisms, the current study focuses
on proving a better understanding of how alcohol
consumption may dierentially aect the con-
sumption of healthy and unhealthy foods among
college students. Little is known on how alcohol
consumption aects actual dietary choices, in
comparison to food consumption at other times.
We hypothesized that college students would be
more likely to consume fast foods and less likely to
consume fruits and vegetables after drinking alco-
hol, compared to at other times.
2. Materials
2.1 Participants
e University of Michigans Institutional
Review Board approved this study prior to data
collection. A sample of ethnically diverse under-
graduates at a large public university in the Mid-
western United States completed an anonymous
on-line survey at their convenience. Respondents
enrolled in the Introductory Psychology Partici-
pant Pool received course credit for completing
the survey, and the sample represents researcher
participant pool allocations for two academic
terms (Fall 2014 and Winter 2015). All partici-
pants completed the survey.
We retained participants who reported ever
using alcohol for the current study (N = 262; 51%
female, M age = 19, SD age = 1), we excluded
77 participants who reported not ever drinking
alcohol. ese participants reported that their
grandparents were Western European (51.1%),
Eastern European (42.7%), East Asian (9.9%),
Arab/Middle-Eastern (5.7%), African-American
(5.3%), South Asian (5.0%), Latino/a or Hispanic
(5.0%), Native American/Alaskan Native (1.1%),
Pacic Islander (1.1%), and Other (5.0%), inclu-
sively. is sample size enables statistical power (1
- B) of .89 for small eects (d = .20) and .99 for
medium (d = .50) and large (d = .80) eects in
two-tailed tests and .94, .99, and .99 respectively
for one-tailed tests of directional predictions (see
Cohen, 1988). We anticipated a medium eect
size, for comparison the average eect size in social
psychology is d = .43 (Richard, Bond, & Stokes-
Zoota, 2003). us, our analyses are adequately
powered.
2.2 Survey
e survey included the Behavioral Risk
Factor Surveillance System (BRFSS) fruit and veg-
etable consumption items, and parallel items on
junk food consumption (including salted snack
foods, candy, sweet desserts, fried fast food, ham-
burgers, pizza, tacos, and soda/pop with sugar).
Participants indicated how many drinks they
consumed on average and answered an additional
set of items based on the BRFSS fruit and veg-
etable and junk food items, "Are you more or less
likely to eat or drink the following kinds of foods
when you drink alcohol" (1 = Much less likely...5
= Much more likely). Participants also answered:
"How frequent are your food cravings when you
drink alcohol, compared to other times?," "How
intense are your food cravings when you drink
alcohol, compared to other times?," "How many
alcoholic drinks does it usually take for you to get
food cravings?," "When you drink alcohol, what
percentage of the time do you:-Have food crav-
ings?/Eat something you are craving?/ Eat some-
thing healthier than what you are craving?" e
last three items were answered with a sliding scale
ranging from 0 to 100 and initially set at 50.
3. Results
Participants reported that their food crav-
ings were more frequent, t(261) = 13.21, p <
.001, d = .82, and more intense, t(261) = 13.18,
p < .001, d = .81, when they consumed alcohol
compared to other times. Participants were less
likely to consume nutritious foods and more like-
ly to consume junk foods when they consumed
alcohol compared to other times (See Table 1).
Participants reported having food cravings 58%
of the time when they were drinking (SD = 26),
eating something they were craving 57% of the
time (SD = 25), and eating something healthier
than what they were craving 27% of the time (SD
= 23). Tendencies to eat something healthier than
what participants were craving were associated
with higher overall fruit and vegetable consump-
tion, r(262) = .170, p = .007, and lower overall
junk food consumption, r(262) = -.161, p = .009.
Although we did not assess self-perceived
levels of intoxication, 48% of women and 50%
of men reported average alcohol consumption
consistent with binge drinking (4 and 5 alcoholic
drinks at one time, respectively; NIAAA, 2004).
e level of average alcohol consumption directly
predicted the increased frequency of food crav-
ings after consuming alcohol, r(301) = .316, p
< .001, the frequency of consuming something
being craved, r(301) = .405, p < .001, and the
increased overall likelihood of consuming junk
foods, r(259) = .157, p = .012. However, the re-
ported level of average alcohol consumption did
not predict increased intensity of food cravings,
eating something healthier than what they were
craving, or fruit and vegetable consumption after
consuming alcohol.
- 311 -
American Journal of Health Studies Vol 29 (4) 2014
4. Conclusion
Alcohol consumption is a risk factor for
unhealthy dietary behaviors and obesity. We dem-
onstrate that the foods students consume when
drinking alcohol are less healthy than what the
same individuals consume at other times. Con-
sumption of fruits and vegetables is lower and
consumption of junk foods is higher comparted
to dietary behaviors at other times. ese ndings
help clarify the behavioral mechanisms underlying
the relationship between alcohol consumption,
unhealthy dietary behaviors, and obesity. Another
contributing factor is the food landscape available
in the late evening (Nelson et al., 2009). Food
providers surrounding university campuses are
likely aware of the types of food students crave
when they are drinking alcohol and likely shape
their oerings to attract such individuals. ese
ndings are consistent with previous work on
by (Lloyd-Richardson et al., 2009) that students
who drank alcohol made less healthy food choic-
es as a result of drinking. is study builds on
those ndings demonstrating that students crave
unhealthy foods and act on those cravings more
often when drinking.
As with all research, this study has
strengths and limitations. Strengths include the
use of standard validated measures for diet re-
call, so that results can be compared to those of
many other studies. Additionally, testing this hy-
pothesis among college students is useful because
they are at most risk of engaging in unhealthy eat-
ing behaviors resulting in weigh gain termed the
“Freshman 15” (Mihalopoulos, Auinger, & Klein,
2008). e “Freshman 15” is the belief that stu-
Food Items M SD t p d
100% PURE fruit juices 2.60 1.07 -6.03 .001 -0.38
Fresh, frozen, or canned fruit 2.16 0.91 -14.90 .001 -0.92
Cooked or canned beans 2.14 0.95 -14.69 .001 -0.91
Dark green vegetables 1.97 0.92 -18.30 .001 -1.13
Orange-colored vegetables 1.99 0.95 -17.60 .001 -1.06
OTHER vegetables 2.08 0.99 -14.96 .001 -0.93
Salted snack foods 3.92 1.00 14.85 .001 0.92
Candy 3.52 1.05 8.04 .001 0.49
Sweet desserts 3.48 1.09 7.02 .001 0.44
Fried fast food 4.09 1.01 17.45 .001 1.08
Hamburgers 3.70 1.10 10.33 .001 0.64
Pizza 4.18 0.88 21.60 .001 1.34
Tacos 3.54 1.11 7.78 .001 0.48
Soda/pop with sugar 3.73 1.15 10.31 .001 0.64
Note: 1= Much less likely, 2 = Somewhat less likely, 3 = About the same, 4 = Somewhat more likely, 5 = Much more
likely
Table 1
Are you more or less likely to eat or drink the following kinds of foods when you drink alcohol?
- 312 -
Kruger.J, Kruger D.
dents gain 15 lbs. during their freshman year of
college. Although, most students do not gain 15
lbs., freshman weight gain was 5.5 times greater
than that experienced by the general population
(Mihalopoulos et al., 2008). In another study,
70% of students experienced weight gain in the
rst semester of their freshman year (Lloyd-Rich-
ardson et al., 2009).
Limitations are related to the self-report
cross-sectional survey design. Participants may
have answered the questions in a socially desirable
manner, though they were instructed to take the
on-line survey in private and were able to com-
plete it at their convenience. Self-report responses
are also subject to inaccurate recall. We also did
not determine the time of day for eating after alco-
hol consumption or at other times. Examination
of dietary patterns by time could assess the inu-
ence of dierential food environments.
Further research should include observa-
tional studies completed at various times of the
day, which document the true number of alco-
holic beverages students consume along with the
type and amount of food they tend to consume
while drinking. Some people are able to resist un-
healthy food cravings, thus interventions to help
reduce the impact of the "drunchies" may improve
student health behaviors and outcomes. Reducing
the amount of unhealthy foods that college stu-
dents consume while drinking alcohol may reduce
obesity and improve other health outcomes.
Acknowledgements
We would like to thank Dr. Jon Elhai at the Univer-
sity of Toledo for his guidance on this manuscript.
References
Cohen, J. (1988). Statistical power analysis for the
behavioral sciences (2nd ed.). New York: Academ-
ic Press.
Beauchamp, G. K., Bertino, M., Burke, D., & En-
gelman, K. (1990). Experimental sodium deple-
tion and salt taste in normal human volunteers.
e American journal of clinical nutrition, 51(5),
881-889.
(Eatstreet.com, 2014),
Ham, L. S., & Hope, D. A. (2003). College stu-
dents and problematic drinking: A review of
the literature. Clinical Psychology Review,
23(5), 719-759. doi: http://dx.doi.org/10.1016/
S0272-7358(03)00071-0
Ilgen, M. A., Schulenberg, J., Kloska, D. D., Czyz,
E., Johnston, L., & O'Malley, P. (2011). Preva-
lence and characteristics of substance abuse treat-
ment utilization by US adolescents: National data
from 1987 to 2008. Addictive Behaviors, 36(12),
1349-1352. doi: http://dx.doi.org/10.1016/j.
addbeh.2011.07.036
Jones, K. A., Chryssanthakis, A., & Groom, M. J.
(2014). Impulsivity and drinking motives pre-
dict problem behaviours relating to alcohol use
in University students. Addictive Behaviors,
39(1), 289-296. doi: http://dx.doi.org/10.1016/j.
addbeh.2013.10.024
Lloyd-Richardson, E. E., Bailey, S., Fava, J. L., &
Wing, R. (2009). A Prospective Study of Weight
Gain During the College Freshman and Sopho-
more Years. Preventive medicine, 48(3), 256-261.
doi: 10.1016/j.ypmed.2008.12.009
Lloyd-Richardson, E. E., Lucero, M. L., DiBello, J.
R., Jacobson, A. E., & Wing, R. R. (2008). e
relationship between alcohol use, eating hab-
its and weight change in college freshmen. Eat-
ing Behaviors, 9(4), 504-508. doi: http://dx.doi.
org/10.1016/j.eatbeh.2008.06.005
Mihalopoulos, N. L., Auinger, P., & Klein, J. D.
(2008). e Freshman 15: is it real? Journal of
American College Health, 56(5), 531-534.
Murray, M. M. (1932). e diuretic action of al-
cohol and its relation to pituitrin. e Journal of
Physiology, 76(3), 379-386. doi: 10.1113/jphysi-
ol.1932.sp002933
Nelson, M. C., Kocos, R., Lytle, L. A., & Perry,
C. L. (2009). Understanding the Perceived De-
terminants of Weight-related Behaviors in Late
Adolescence: A Qualitative Analysis among Col-
lege Youth. Journal of Nutrition Education and
Behavior, 41(4), 287-292. doi: http://dx.doi.
org/10.1016/j.jneb.2008.05.005
Wood, P. K., Sher, K. J., & Rutledge, P. C. (2007).
College Student Alcohol Consumption, Day of
the Week, and Class Schedule. Alcoholism: Clini-
cal and Experimental Research, 31(7), 1195-1207.
doi: 10.1111/j.1530-0277.2007.00402.x
Yeomans, M. R. (2004). Eects of alcohol on food
and energy intake in human subjects: evidence for
passive and active over-consumption of energy.
British Journal of Nutrition, 92(S1), S31-S34.
Yeomans, M. R. (2010). Alcohol, appetite and energy
balance: Is alcohol intake a risk factor for obesity?
Physiology & Behavior, 100(1), 82-89. doi: http://
dx.doi.org/10.1016/j.physbeh.2010.01.012
... Further, although 70% of Americans drink alcohol at least once a year (NIAA 2021), and although food and alcohol choices influence one another (e.g., food-wine pairings; Harrington 2007), alcohol consumption is almost never studied in consumer research (for an exception see Cornil, Chandon, and Krishna 2017). Relatedly, although beverages in general influence food choices (Kruger and Kruger 2015), increase fullness (Lappalainen et al. 1993), and are typically available when "free-living eating," beverages other than plain water are often not available in "lab eating." Additionally, in "free-living eating," choice set options are often constructed by consumers either in advance through planning (e.g., shopping lists; Block and Morwitz 1999; Suher, Huang, and Lee 2019) or in the moment (e.g., searching the pantry; impulse shopping). ...
Full-text available
Article
Food consumption and its physiological, psychological, and social antecedents and outcomes have received considerable attention in research across many disciplines, including consumer research. Although researchers use various methods to examine food decision-making, many insights generated stem from observing eating choices in tightly controlled lab settings. Although much insight can be gained through such studies (or “lab eating”), it is apparent that many factors differ between such settings and everyday consumption (or “free-living eating”). This article highlights key differences between “lab eating” and “free-living eating,” discusses ways in which such differences matter, and provides recommendations for researchers regarding how and when to narrow the gap between them, including by enriching lab studies in ways inspired by free-living eating. Besides suggesting how researchers can conduct studies offering a deeper understanding of eating patterns, we also highlight practical implications for improving food consumption for consumers, marketers, and policymakers.
... Like previous studies, the present study evidenced that males (34.3%) choose fast food meals in a higher frequency (1-3 times per week). Fast food is energy-dense, low nutritional value food, very popular among students, leading to obesity [31], [32]. In Romania, even though healthy food is available on campus and off-campus, and the population's socioeconomic status is good, study shows that a segment of students choose to eat unhealthy food, often when socializing with their peers, which is a reason for nutrition education in university students. ...
Full-text available
Article
Poor eating habits and substance abuse are major public health concerns among young adults transitioning into university life. This study's objective was to assess Romanian students' alcohol consumption and correlate it with other lifestyle factors. We performed a cross-sectional study of 1212 students with a mean age of 21.1 ± 2.4 years. We applied a valid online questionnaire to evaluate and statistically analyze the interrelation between alcohol consumption and lifestyle factors by multivariate statistical analysis. The prevalence of alcohol consumption among the student population enrolled in the study was 79.9%. Multiple regression showed that alcohol consumption was positively associated with gender (p < 0.001), level of physical activity (p = 0.009), number of cigarettes (p < 0.001), and fast-food consumption (p < 0.001), and negatively associated with sleep (p = 0.012) and study hours (p < 0.001). The study revealed a high prevalence (18%) of binge drinking among males. The frequent use of illicit drugs is associated with alcohol consumption (p < 0.001) but present at low levels (1.6%). The study evidenced a high prevalence of alcohol consumption in students, especially in males, and poor food behavior related to the intake of vegetables and fruits. Health promotion campaigns regarding the harmful effects of alcohol, smoking, poor nutrition, and ongoing illicit drug prevention campaigns are needed to improve students' performances.
Full-text available
Article
This study used a four-factor model of impulsivity to investigate inter-relationships between alcohol consumption, impulsivity, motives for drinking and the tendency to engage in alcohol-related problem behaviours. 400 University students aged 18-25 completed an online survey consisting of the following measures: Urgency, Premeditation, Perseverance and Sensation Seeking Scale (UPPS) to measure impulsivity; Student Alcohol Questionnaire to assess drinking quantity, frequency and rates of problem behaviours; Drinking Motives Questionnaire to assess motives for drinking. The majority of the sample (94.5%) drank alcohol at least monthly. Path analysis revealed direct effects of urgency, sensation seeking and premeditation, as well as the quantity of alcohol consumed, on the tendency to engage in risky behaviours with negative consequences. The effect of urgency was mediated by drinking for coping motives and by a combined effect of drinking for social motives and consumption of wine or spirits. Conversely the effect of sensation seeking was mediated by the quantity of alcohol consumed, irrespective of drink type, and the effect of premeditation was mediated by the consumption of wine and spirits, in combination with enhancement motives. Sensation seeking, urgency and lack of premeditation are related to different motives for drinking and also demonstrate dissociable relationships with the consumption of specific types of alcohol (beer, wine and spirits) and the tendency to engage in risky behaviours associated with alcohol consumption. Screening for high levels of urgency and for severe drinking consequences may be useful predictors of alcohol-related problems in UK University students aged 18 to 25years.
Full-text available
Article
Problem drinking during the college years is a significant public health concern. The goal of the current review was to examine the primary psychosocial factors that predict problem drinking in college students. Variables examined included demographic variables, personality, drinking history, alcohol expectancies, drinking motives, stress and coping, activity involvement, and peer and family influence. Evidence from studies of college drinking indicated that the variables associated with college drinking seem to vary at levels dealing with one's personality and coping mechanisms, one's thought processes about drinking, and the environment. It seems that expectancies and drinking motives may serve as explanations for the pathways from certain personality types (i.e., sensation seeking and neurotic) to problem drinking in the college setting. Factors that predicted future drinking problems after college were also examined. Overall, it seems that interventions and prevention programs would need to reach college students at all three levels--the environment, individual personality traits, and cognitive processes. Future research should address the limitations in the previous research as well as test comprehensive models of college drinking.
Article
Although many adolescents use and abuse illicit drugs, few of those who could benefit from substance abuse treatment ever receive these services. The present study examines the prevalence of utilization of substance abuse treatment in national samples of adolescents over the past 22 years and identifies characteristics associated with receipt of these services. Monitoring the Future data on lifetime utilization of substance abuse treatment was available for 12th grade students who reported any lifetime illicit drug use from 1987 to 2008 (N=25,537). After describing the prevalence of treatment utilization over this time period, logistic regression was used to examine potential predictors of treatment utilization. The overall prevalence of treatment utilization has remained relatively unchanged over the past 22 years. In multivariable models, adolescents reporting a greater frequency of lifetime use of marijuana or cocaine were more likely to receive substance abuse treatment. Additionally, substance abuse treatment utilization was more likely in those who received other mental health services. Despite increased evidence for the effectiveness of substance abuse treatment, utilization of these services by adolescents has remained low and relatively stable over the past 22 years. Attempts to increase utilization of substance abuse treatment services would likely benefit from building on existing connections with mental health treatment.
Article
The increased recognition that the worldwide increase in incidence of obesity is due to a positive energy balance has lead to a focus on lifestyle choices that may contribute to excess energy intake, including the widespread belief that alcohol intake is a significant risk factor for development of obesity. This brief review examines this issue by contrasting short-term laboratory-based studies of the effects of alcohol on appetite and energy balance and longer-term epidemiological data exploring the relationship between alcohol intake and body weight. Current research clearly shows that energy consumed as alcohol is additive to that from other dietary sources, leading to short-term passive over-consumption of energy when alcohol is consumed. Indeed, alcohol consumed before or with meals tends to increase food intake, probably through enhancing the short-term rewarding effects of food. However, while these data might suggest that alcohol is a risk factor for obesity, epidemiological data suggests that moderate alcohol intake may protect against obesity, particularly in women. In contrast, higher intakes of alcohol in the absence of alcohol dependence may increase the risk of obesity, as may binge-drinking, however these effects may be secondary to personality and habitual beverage preferences.
Article
Identify key factors underlying college weight gain, nutrition, and physical activity. Six focus groups and one-on-one interviews. Large, public Midwestern university. Fifty full-time freshman and sophomore students. Factors influencing weight and weight-related behaviors among undergraduates. Qualitative analysis using a specific thematic approach, identifying themes appearing consistently across transcripts from recorded sessions. Major themes that emerged in describing important influences on weight, dietary intake, and physical activity included: unhealthful food availability on campus, snacking, late-night eating, alcohol-related eating, eating because of stress/boredom, and food in student dorm rooms. Other factors related to physical activity included: negative experiences using campus recreation facilities; poor weather; and lack of time/time management, motivation, and social support for exercise. A wide range of factors may underlie weight gain and unhealthful diet and physical activity patterns during the college years. Young adulthood is an important and overlooked area for obesity prevention efforts. Universities need to take an active role in designing and evaluating weight-related health promotion intervention strategies focusing on a variety of targets, including individual-, social-, and environmental-level influences.
Article
To assess the prevalence of weight gain among male and female college freshmen. Study 1 examined weight change over freshman and sophomore years among 904 students attending a state university in Indiana, from 2002-2004. Study 2 examined weight and BMI change over the freshman year among 382 students attending a private university in Rhode Island, from 2004-2006. 77% of Study 1 participants and 70% of Study 2 participants gained weight during their freshman year, largely during the first semester. In Study 1, weight gain averaged 3.5 kg in females and males; in Study 2, weight gain averaged 1.6 kg for females and 2.5 kg for males. Students continued to gain weight their sophomore year, with females 4.2 kg and males 4.3 kg heavier than at start of college. Overweight/obesity rates increased from baseline to end of freshman year for Study 1 (21.6% to 36%) and Study 2 participants (14.7% to 17.8%). The first years of college may be a critical developmental window for establishing weight gain prevention efforts.
Article
This study aims to improve understanding of how alcohol consumption in college freshmen affects eating patterns before, during, and after drinking, as well as its relation to body weight change. Two hundred eighty-two college freshmen (61% female; 59% Caucasian) completed measures of alcohol use, measured body mass index (BMI), and eating and activity habits before, during and following drinking episodes. Students were categorized by drinking status (non-drinker, low-risk, and moderate/high-risk) in order to explore group differences. Seventy-five percent of the sample reported past-month alcohol consumption, with 65% (N=134) of these categorized as "low-risk" drinkers and 35% (N=72) as "moderate-risk" drinkers. Moderate-risk drinkers were more likely than low-risk drinkers to report increases in appetite after drinking, with nearly half of students reporting overeating and making unhealthy food choices following drinking. Moderate-risk drinkers also demonstrated significant increases in 1st semester BMI change, relative to non-drinkers and low-risk drinkers. Eating patterns for a significant number of college students are altered before, during, and following drinking episodes, which related to change in freshman year BMI.
Article
To examine the sensory effects of extreme sodium depletion in humans, 10 normal volunteers were fed a very-low-sodium diet and were treated with diuretics for 10 d. Urine samples were collected and blood was drawn for hormone analyses. Taste tests included threshold and intensity judgments of salt (NaCl) and sucrose and preferences for salt and sucrose in foods. Subjects also rated the pleasantness of 29 foods listed on a questionnaire. Substantial sodium depletion was induced in all subjects. Salt thresholds decreased in a majority of the subjects whereas preference judgments for salt in foods tended to be greater during the depletion period. The changes in pleasantness of the 29 foods revealed that saltier foods were substantially more attractive during the depletion period than during the pre- and postdepletion periods. These data indicate that experimental sodium depletion in humans is followed by moderate sensory changes and an increased preference for salty foods.