Background: In transitioning from adolescence to adulthood, college students are faced with significant challenges to their health habits. Independence, stress, and perceived lack of time by college students have been known to result in poor eating and exercise habits, which can lead to increased disease risk.
Objective: To assess the feasibility and to determine preliminary efficacy of an electronic wellness program in improving diet and physical activity in college students.
Methods: A 24-week diet and physical activity program was delivered via email to 148 college students. The intervention involved weekly, tailored, and interactive diet and physical activity goals. The control group received nondiet and nonexercise-related health fact sheets. Anthropometric and blood pressure measurements, as well as food frequency and physical activity surveys were conducted at baseline, week 12, and week 24. Students’ choice of fruit as a snack was also monitored at study visits.
Results: Students were 18-20 years old, 69% female, and from a diverse college campus (46% Caucasian, 23% Asian, 20% African American, 11% other). At week 24, 84% of students reported reading at least half of all emails. Mean change (standard error [SE]) from baseline of saturated fat intake was marginally significant between the treatment groups at week 24, 0.7 (SE 0.42) % kcal for control and -0.3 (SE 0.30) % kcal for intervention (P=0.048). A significant difference in percent of snacks chosen that were fruit (χ21, N=221 = 11.7, P<0.001) was detected between the intervention and control group at week 24.
Conclusions: Use of an electronic wellness program is feasible in college students and resulted in a decrease in saturated fat intake and an increase in observed fruit intake compared to a control group.
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The phrase “freshman 15” was coined to describe weight gain associated with poor health habits in US college students. Studies of this phenomenon have identified an average of a 1-4 kg weight gain during the first year of college [- ]. In the United States, this can result in increased proportions of students being classified as overweight, from 21% at the beginning of freshman year to 32% at the end of the first school year [ ]. This weight gain can lead to obesity in adulthood. Specifically, the risk of being obese as an adult is 4 times as high for an overweight or obese adolescent, compared to a normal-weight adolescent [ ]. Obese adolescents are at increased risk for disease as an adult, particularly cardiovascular disease [ - ].
College is a time of increasing independence and growth. During this period, students may make poor diet choices such as skipping breakfast, consuming salty and sugary snacks, and increasing alcohol consumption, and may fail to meet physical activity guidelines [, , - ]. According to 2013 data from the National College Health Assessment, only 6% of college students report eating at least 5 servings of fruits and vegetables daily and less than half meet recommendations for exercise [ ]. Such health behaviors result in a generation entering adulthood at risk for obesity, cardiovascular disease, and cancer. Clearly, there is a need to improve diet and exercise habits of college students.
Contemporary college students are a technologically wired generation having been born and raised in the age of home computers and portable electronic devices. Electronic health interventions (eHealth) have the advantage of reaching larger numbers of individuals with fewer resources than face-to-face interventions . The duration of eHealth studies in college students has ranged from 30 days [ ] to 2 years [ ], with most studies reporting a duration of 12 weeks [ - ]. A variety of eHealth methods including virtual technology, graded classroom assignments, and Web-based coaching using Facebook, email, and SMS text messaging, have been successful in significantly increasing physical activity in college students [ , , , ]. SMS text messaging requires short messages and, therefore, education and graphics are limited. Facebook has also been used to encourage weight loss in college students [ , ]. Facebook allows for graphics and social networking but control of privacy may be an issue for users. Weight loss programs employing other Internet technology have been used successfully in overweight and obese college students [ , ]. Previous attempts to prevent excess weight gain by college students using eHealth have had mixed results [ , - ]. Green [ ] and Kattelmann [ ] were able to demonstrate improvements in diet and physical activity behaviors, but no difference in weight gain or body measures, while Gow [ ] and Levitsky [ ] demonstrated the prevention of increases in body mass.
The objective of this pilot study was to determine to what extent a 24-week email-delivered health intervention program, which was designed to promote healthy diet and physical activity habits in college students, would be received by a diverse sample of college students and to report preliminary efficacy for diet, physical activity, and body measures. In terms of feasibility, we hypothesized that the enrollment goal (135 college students) would be met within the first 6 weeks of the semester and that > 60% of students enrolled would report reading at least 50% of the emails. Moreover, it was expected that after 24 weeks, those who received the eHealth intervention would consume more fruits and vegetables, less sugar and fat, and would report more physical activity than those in the control group. Furthermore, changes in diet and physical activity were anticipated to result in attenuation of body composition measures and improved levels of fitness.
Approval was obtained through the University Institutional Review Board (IRB). Eligible students were recruited from a large eastern university campus utilizing IRB-approved announcements via campus list-serve technology. Interested students contacted the study team for an initial phone screening to determine eligibility. Eligible students were then scheduled for an in-person informed consent visit. Students were eligible if they were enrolled in the university during the semester the study was conducted, were 18-20 years old, and had access to email. Informed consent was conducted by trained research staff at the first study visit, prior to any study procedures. Students were excluded if they were pregnant, lactating, reported a history of an eating disorder or bariatric surgery, were currently following a diet treatment plan for weight loss, or were participating in a research study that affected health behaviors. Eligible participants were randomized 2:1 and stratified by body mass index (BMI) and gender to the intervention or control group. This ratio ensured that dropouts did not affect the ability to demonstrate the feasibility of this pilot program .
Numbering of study weeks was linked to the baseline visit when the individual participant was randomized and began the electronic intervention, rather than on the academic calendar. Study enrollment was staggered over the first 6 weeks of the fall semester. The schedule for subsequent study visits was staggered similarly, to coincide with the follow-up surveys included in the intervention program. Participants were given a $25 gift card to a local department store for completing baseline, week 12, and week 24 study visits. Students who completed all visits were entered into a raffle for a $250 gift certificate.
Trained research assistants conducted bioelectrical impedance analysis, anthropometric, and blood pressure measurements. All anthropometric data were performed in triplicate on the right side. Students were asked to refrain from exercise for 2 h, and maintain a 4-h fast (2 h for water, 12 h for caffeine and alcohol) prior to anthropometric measurements. A digital scale (Scale-Tronix, serial number 5002-27460) was used to weigh participants in underclothes to the nearest 0.1 kg. A wooden portable stadiometer (Shorr Productions) was used to measure participants’ height to the nearest millimeter, with the posterior heel, buttocks, shoulder blades, and head touching the vertical board of the stadiometer with the head in the Frankfurt plane . The accuracy and precision of the scale and stadiometer were verified daily using two 20 kg weights and a 160 cm rod, respectively. A Gulick II fiberglass tape measure (Country Technology, model 67020) was used to measure body circumference to the nearest millimeter. Mid-neck circumference (NC) was measured between mid-cervical spine and mid-anterior neck just below the laryngeal prominence with the head in the Frankfurt plane [ ]. Waist was measured at the right superior iliac crest [ ]. Hip circumference was measured at the widest area across the buttocks [ ]. Waist-to-hip ratio (WHR) was calculated by dividing the waist circumference (WC) measure in cm by the hip circumference measure in cm.
In preparation for bioelectrical impedance analysis (BIA), participants were questioned about fasting status, and were asked to empty their bladder. Urine samples were collected for pregnancy tests. All external metal from the right side of the body and any personal electronic devices were removed, to avoid interference in the flow of the electrical charge from the BIA. Pregnancy, history of seizures, and heart arrhythmias were considered exclusion criteria for BIA. Two participants were deferred, one for history of arrhythmias and a second for history of seizure. A third student failed to meet fasting conditions and did not complete baseline BIA. Participants rested supine avoiding skin-to-skin contact during the entire procedure. BIA was conducted according to the manufacturer’s instructions using an ImpediMed DF50 bioelectrical impedance analyzer. Briefly, skin of the right hand, wrist, foot, and ankle were scrubbed with alcohol wipes prior to placing 4 electrodes (1) midway between the distal ulna and the dorsal radius of the wrist; (2) 5 cm from the wrist electrode, toward the fingers; 3) midway between the distal tibia and dorsal fibula; and 4) 5 cm from the ankle electrode, toward the toes. Participant height, weight, sex, and age were entered into the software before conducting the BIA. Estimates of body composition were obtained from the BIA software.
Diet and Physical Fitness
Students completed demographic, psychosocial, diet, and physical activity surveys online. The diet survey consisted of a food frequency questionnaire adapted from the Block Food Frequency Questionnaire . The physical activity questionnaire was adapted from the Cross-Cultural Activity Patterns Questionnaire (CCAPQ) [ ]. Reliability and validity have been established for both [ , ]. Exercise was defined as moderate to vigorous physical activity as categorized in the CCAPQ [ ].
To prevent adverse reactions from fasting, each participant was offered water and snacks prior to the Queen’s College Step Test . Snacks included fruit (6 types), cookies (4 types), and crackers and chips (11 types). Consumption of the snack was used to evaluate whether the eHealth messages resulted in actual diet change. Snacks were measured before serving to participants and again after the study visit. Postweight was subtracted from preweight to obtain total intake. Because consumption of snack was optional, frequency of consumption was used (the number of times students ate >50% of a snack divided by total snacks chosen at that visit) for each treatment group.
Seated blood pressure was measured under resting conditions using a 10 series automated blood pressure monitor (Omeron Healthcare) per the manufacturer’s instructions.
The Queen’s College Step Test procedure required participants to step up and down on a 41.3 cm wooden step for 3 min at 24 steps/min for males and 22 steps/min for females  while wearing an Omron HR-100C heart rate monitor transmitter. Step cadence was set using a metronome (Korg, model MA-30) and monitored using a Survivor III stopwatch (Accusplit, model S3MAGXLBK). Maximum oxygen utilization (VO2max) in mL/kg/min, was estimated for men (VO2max= 111.33 − 0.42 × heart rate in bpm) and women (VO2max= 65.81 − 0.1847 × heart rate in bpm) [ ]. Two intervention group members were deferred from the Queen’s College Step Test due to uncontrolled asthma and injury.
The eHealth intervention consisted of A Lifestyle Intervention via Email (ALIVE), an evidence-based Web-based behavior change program created and managed by NutritionQuest  and modified by the authors for use with college students. Modifications primarily eliminated or replaced work and family-oriented language. For example, references to work and family were rewritten to include campus or apartment living and dining. Social and cognitive principles inherent to ALIVE included goal setting, a focus on individual choice, direct information and goal relevance for each learner, overcoming barriers, specific action-based advice such as establishing walking groups, salience of cues, building on prior learning, repetition of core messages, and repeated practice of new behaviors to transform them into sustained habits. There was an emphasis on small, achievable, and cumulative goals, for which accomplishment builds the participant’s self-efficacy to make changes and can enhance long-term maintenance.
After completing diet, physical activity, medical history, and stages of change surveys, participants received feedback comparing their responses to recommended levels of (1) fat and sugar intake, (2) fruit and vegetable intake, and (3) physical activity. As part of the ALIVE program, all participants were encouraged to select a goal related to the 3 feedback topics. Once randomized, participants in the intervention group chose one of these topics as the focus for weekly messages offering tailored small-step goals, tips for overcoming barriers to goals, health information, and social support. Web links in the intervention email also led students to their personal account on the ALIVE website, where educational information and feedback on progress were offered. Follow-up diet and physical activity surveys were delivered to participants every 12 weeks as part of ALIVE. A 24-week duration was chosen to best coincide with the school year. The intervention group received the adapted ALIVE program via weekly emails. The control group received weekly information related to nondiet, nonexercise health topics, such as distracted driving, sleep hygiene, and smoking cessation.
Study data were collected and managed using Research Electronic Data Capture (REDCap) hosted at Children’s National Medical Center. REDCap is a secure, Web-based application that provides (1) an interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for data downloads to common statistical packages, and (4) procedures for importing data from external sources .
Demographic and baseline characteristics of students in the intervention group were compared to those in the control group using t test for continuous variables and chi-square for categorical variables. Chi-square was also used to determine differences in snack choice categories between the intervention and control groups. Changes over time were analyzed by repeated measures analysis of variance using a 2 (treatment: intervention, control) × 3 (time: baseline, week 12, week 24) factorial treatment design and sex and baseline BMI were controlled as covariates. Goodness-of-fit statistics were used to select the repeated measures structure that best fit the data. Significance was set at P<.05. The primary analysis was an intent-to-treat approach (ITT) with the last observation carried forward to fill in missing data. Secondary analyses were performed on a subgroup of completers. These participants were compliant with inclusion/exclusion criteria throughout the study period and completed all study visits and questionnaires.
An IRB-approved recruitment email was sent to 10,370 students, 693 of whom responded. The recruitment goal for this pilot study was 135 to retain 100 students with an expected dropout rate of 25%. The study team was able to contact 317 students by the last enrollment day; 99 did not respond and 218 were screened for eligibility, resulting in 99 students randomized to the intervention group and 49 to the control group (). Mean age (standard error [SE]) was 19.7 (SE 0.06) years. Study participants were primarily female and racially diverse ( ). The participants were generally healthy, reporting low rates (1-5%) of hypertension, hypercholesterolemia, gastrointestinal disease, and anemia. The highest prevalence of disease was asthma (17%). Dietary supplement use was reported by 31% of students. Less than 5% of students were smokers. Other than previous research experience, baseline characteristics did not differ between treatment groups ( ).
Students who dropped out (intervention group: n=31, control group: n=11) had significantly higher mean BMI (24.2 [SE 0.62] kg/m2, P=.008), WC (85.3 [SE 1.55] cm, P=.002), WHR (0.86 [SE 0.0067] cm, P=.004), and NC (34.6 [SE 0.50] cm, P=0.006), yet lower energy intake (19.8 [SE 1.31] kcal/kg, P=.011), than those who completed all visits (BMI: 22.4 [SE 0.32] kg/m2, WC: 79.7 [SE 0.79] cm, WHR: 0.84 [SE 0.0043] cm, NC: 33.0 [SE 0.29] cm, and energy intake: 24.0 [SE 1.04] kcal/kg).
|Age (years): mean (SEc)||19.8 (0.07)||19.6 (0.1)||.15|
|Female||68 (69)||33 (67)|
|Male||31 (31)||16 (33)|
|White, non-Hispanic||47 (47)||21 (43)|
|Asian||23 (23)||11 (22)|
|African-American||16 (16)||13 (26)|
|Hispanic||7 (7)||1 (2)|
|Mixed||4 (4)||2 (4)|
|Other||2 (2)||1 (2)|
|Freshman||3 (3)||5 (10)|
|Sophomore||38 (39)||23 (48)|
|Junior||53 (55)||19 (40)|
|Senior||3 (3)||1 (2)|
|Transportation to campus||.55|
|Live on campus||45 (46)||28 (57)|
|Walk/run||28 (29)||11 (22)|
|Drive||17 (17)||8 (16)|
|Bike||8 (8)||2 (4)|
|Smoke||5 (5)||2 (4)||.76|
|Take vitamins||33 (34)||14 (29)||.58|
|Past research participant||48 (50)||10 (20)||.001|
aData are presented as total count and percent (intervention: N=99, control: N=49) unless otherwise noted. School year N=97 intervention, N=48 control. Transportation to campus N=98 intervention. Take vitamins: N=48 for control group. Past research participant: N=96 for intervention group.
bStudent\'s t-test for age; all others are chi-square analysis of differences between groups.
cSE: standard error of the mean
At baseline, 22% (33/148) were overweight/obese (BMI ≥25 mg/kg2). No significant differences were detected between treatment groups for students meeting health guidelines at baseline (). Baseline anthropometric data did not differ significantly between groups ( ). With the exception of WHR, most met recommendations for body measures. Further analysis of the WHR data revealed that all of those with elevated WHR were female, and of the 83 females with elevated WHR, 25 had WC measures that were below the recommended cut-off for disease risk (≤88 cm). Mean body fat mass percent (FM%) by sex was 29 in females and 20 in males.
Diet and Fitness
At baseline, 88% of participants (130/148) reported consuming <5 servings of fruits and vegetables daily, 59% (87/148) consumed >10% of their kcal from saturated fat; however, 91% (135/148) met/exceeded 150 min/week of moderate-vigorous exercise. Prevalence of measured high blood pressure (18%, 26/148) was higher than self-reported blood pressure (0.7%, 1/148). No significant differences were detected between treatment groups for students meeting diet and physical fitness guidelines at baseline ().
|FM% c (female: <32, male: <22)||54 (55)||33 (66)||.30|
|BMId (18.8-25g/m2)||75 (76)||40 (80)||.60|
|Neck circumference (female: <34 cm, male: <37 cm)||75 (76)||37 (74)||.68|
|Waist circumference (female: ≤88 cm, male: ≤102 cm)||79 (81)||46 (92)||.08|
|Waist-to-Hip ratio (Female: <0.8, male: <1.0)||43 (44)||21 (43)||.91|
|Saturated fat (<10% daily kcal)||38 (38)||23 (46)||.58|
|Total fat (<30% daily kcal)||97 (98)||49 (98)||.99|
|Sugar (<10% daily kcal)||56 (57)||25 (50)||.45|
|Fruit/vegetable (>5 servings/day)||13 (13)||5 (10)||.93|
|Blood pressure (<120/80 mmHg)||83 (84)||39 (78)||.35|
|Exercisee (≥150 min/week)||88 (89)||47 (94)||.31|
|VO2maxf (mL/kg/min)||56 (58)||34 (69)||.17|
aData are presented as total count and percent (intervention: N=99, control: N=49) except FM% (Intervention: N=96) and VO2max (intervention: N=94). Dietary intake data and exercise are self-reported.
bChi-square analysis of differences between groups (P<.05).
cFM%: body fat mass percent
dBMI: body mass index
eExercise: min/week of moderate to vigorous physical activity
fVO2max: maximum oxygen utilization. Met: >35, >33, >45, and >42 mL/kg/min for 19-year-old female, 20-year-old female, 19-year-old male, and 20-year-old male, respectively .
Repeated measures ANOVA with BMI and sex as covariates revealed a marginally significant linear time by treatment interaction for percent of energy from saturated fat, F1,372=3.94, P=.048 (). This was true for ITT analysis and completers. This indicates that the increase in saturated fat intake by the control group (0.3 [SE 0.30] % of kcal) was different from the decrease in saturated fat intake by the intervention group (0.7 [SE 0.42] % of kcal), P=.048. No significant effect of BMI or sex was detected for these variables. Further analysis of the change in saturated fat intake according to the Dietary Guidelines for Americans (DGA) recommendation (<10% of kcal) revealed a significant linear interaction of time and meeting/not meeting recommendations for the intervention group (P<.001). Thus, the decrease in saturated fat intake in the intervention group was largely due to those whose intake was higher than recommended ( ). Additionally, the mean saturated fat intake for completers in the intervention group did not meet the DGA guidelines at baseline but did reach DGA levels by study completion. In contrast, the three completers in the control group who consumed saturated fat in excess of DGA guidelines at baseline never achieved DGA levels ( ). No significant treatment by time interactions were detected for dietary intake of sugar, or fruit and vegetables, or for anthropometric or fitness variables ( ).
No significant difference in consumption of fruit or non-fruit snacks at baseline (P=.22) and week 12 visits (P=.06) was detected between the intervention and control groups. At week 24, significantly different consumption of fruit and non-fruit snacks (χ21, N=221 = 11.7, P<.001) was detected between the intervention and control groups ().
|Variable||Baselinea||Week 12a||Week 24a|
|FM%b||24.3 (0.50)||25.0 (0.68)||24.9 (0.50)||25.2 (0.68)||24.6 (0.50)||24.9 (0.68)||0.36|
|BMIc (kg/m2)||23.1 (0.38)||22.8 (0.54)||23.1 (0.38)||22.8 (0.54)||23.2 (0.38)||22.8 (0.54)||0.80|
|NCd (cm)||34.4 (0.14)||34.4 (0.19)||34.4 (0.14)||34.4 (0.19)||34.5 (0.14)||34.6 (0.19)||0.34|
|WCe (cm)||82.2 (0.49)||79.9 (0.68)||81.7 (0.49)||80.4 (0.68)||81.9 (0.54)||80.1 (0.75)||0.41|
|WHRf||0.85 (0.004)||0.84 (0.006)||0.85 (0.004)||0.84 (0.005)||0.85 (0.004)||0.84 (0.006)||0.21|
(% total kcal)
|8.2 (0.29)||7.2 (0.41)||8.0 (0.32)||7.5 (0.42)||7.8 (0.34)||8.1 (0.45)||0.14|
(% total kcal)
|10.6 (0.84)||12.1 (1.2)||10.2 (0.84)||12.0 (1.1)||10.1 (0.84)||10.6 (1.2)||0.32|
|2.6 (0.17)||2.7 (0.23)||2.4 (0.14)||2.3 (0.20)||2.4 (0.14)||2.4 (0.20)||0.64|
|SBPi (mmHg)||111 (0.72)||111 (1.2)||111 (0.89)||111 (1.2)||110 (0.89)||110 (1.2)||0.92|
|DBPj (mm Hg)||71.9 (0.72)||70.8 (1.0)||71.8 (0.72)||70.7 (1.0)||71.0 (0.72)||70.2 (1.0)||0.80|
|795 (58)||844 (80)||736 (58)||705 (80)||788 (58)||801 (80)||0.63|
|41.4 (0.59)||42.6 (0.81)||41.1 (0.44)||42.1 (0.59)||40.8 (0.47)||42.4 (0.63)||0.83|
aInt = Intervention. Cont = Control. Overall N=99 Int, N=49 Cont except FM% N=96 for Int, and VO2max N=94 for Int. Mean (standard error) derived by ANOVA repeated measures intent-to-treat analysis, adjusted for sex and baseline BMI.
bFM%: Fat Mass%
cBMI: body mass index
dNC: neck circumference
eWC: waist circumference
fWHR: waist-to-hip ratio
gLinear time by treatment interaction: F1,372=3.94, P=.048
hFruit/veg: fruit and vegetable
iSBP: systolic blood pressure
jDBP: diastolic blood pressure
kExercise: minutes per week of reported moderate-vigorous activity
lVO2max: maximum oxygen utilization
Among the 99 students in the intervention group, 32 chose goals to reduce fat and sugar intake, 32 chose to improve fruit and vegetable intake, and 35 chose to focus on physical activity. Of the 109 students who answered the satisfaction survey at week 24, 91 (84%) reported reading at least half of the study emails (control: n=35, intervention: n=56).
This study demonstrated the feasibility of ALIVE in a college population, by exceeding the recruitment goal with 106 participants who completed the study, a majority of whom reported reading most of the emails. ALIVE was effective in decreasing consumption of saturated fat and increasing the frequency of choosing fruit as a snack by a diverse sample of college students.
Similar to other eHealth interventions in college students, small improvements in dietary intake but no changes in body measures were seen in the current study [, ]. Differences between treatment groups in dietary intake of sugar, energy, vegetables, or for anthropometric or fitness variables were not detected. However, attenuation of BMI, WC, NC, WHR, body fat, blood pressure, sugar intake, and VO2max in this study indicated that student health habits had not worsened. As seen in , large percentages of students required improvements in dietary sugar, fruit/vegetable, and saturated fat intake, whereas most students met requirements for body measures and physical activity. Additional studies are needed to assess the efficacy of eHealth in reinforcing optimal behaviors, particularly for those who do not meet health recommendations.
That the intervention group was not more successful compared to the control in attenuating health habits may arise from the insufficient power to detect these treatment effects, and the bias of repeated diet and physical activity surveys and body measurements in both treatment groups. According to previous studies, college students who are measured but receive no feedback, education, or intervention, show an increase in BMI, FM%, WC, and dietary fat intake, and decreased vegetable and fruit intake over time [, , ]. In the present study, control participants also received feedback and were encouraged to select a goal at baseline. Additionally, students in the intervention group were able to switch between the fruit and vegetable, fat and sugar, and physical activity health behavior tracks at any time. Thus, it is likely that only some students had the benefit of the full program of coaching. This could have weakened the effect, requiring a much larger sample size. It should also be noted that week 12 visits coincided with fall semester finals, which can be a high-snack, low-activity period in college life.
Although some eHealth programs were able to prevent increases in weight gain by college students, we were not able to support this finding [, ]. However, the current study did demonstrate the importance of screening for high blood pressure in college populations and the importance of carefully selecting anthropometric measurements in order to detect disease risk. This study is the first to report NC in a diverse college sample [ , ]. A relatively unused measure, NC has been associated with obesity and cardiovascular disease risk [ , ]. In the current sample, approximately 25% of students were categorized as obese using NC, which is comparable to that identified by BMI. WC and WHR are often used to assess central obesity and are predictive of insulin resistance [ ] and cardiovascular disease [ , , ]. In our sample, 30% of females with elevated WHR had WC measures in the healthy range, indicating that WHR may not be a reliable indicator of central adiposity in a college-aged sample. NC is less intrusive than WC and should be further explored as an indicator of adiposity and disease risk in college students.
The students who failed to complete the study had significantly higher indicators of overweight/obesity, although only NC was clinically significant (higher and over recommendation) compared to those who dropped out. This may indicate that the program may not be well received by those with higher body weight.
Diet and Fitness
Less than half of the participants met the dietary guidelines for saturated fat intake at baseline, whereas the amount of total fat consumed met recommended levels. Thus, college students could benefit from interventions that alter food choices to improve the fat profile. The ALIVE program used in this study was designed to encourage and measure dietary changes in fat, sugar, fruits, and vegetables. We detected a linear time by treatment interaction, in which saturated fat intake worsened for the control group compared to the intervention group. Considering that the dietary guideline is 10% or less of calories from saturated fat , the observed difference in intake between groups of 1% of calories is clinically important.
Only 6% of college students report eating at least five servings of fruits and vegetables daily . This is further supported by the current study, in which less than 15% of participants met the dietary guideline for fruit and vegetables. Snacks are a convenient way to bolster fruit and vegetable intake. Many are “self-packaged” and easy to carry (eg, oranges, apples, bananas). Given the limits of self-reported data, consumption of fruit as a snack was objectively measured in this sample of college students. Improved selection and consumption of fruit when offered as a snack was found after 24 weeks of the ALIVE program, whereas self-reported data showed no change in fruit and vegetable intake.
Lack of fitness as indicated by VO2max and poor dietary patterns may explain the large portion of students having excessive weight and body fat. For example, almost half of the students in our study exceeded the World Health Organization recommendation for sugar intake, while a majority of students did not meet DGA guidelines for consuming fruit and vegetables. Dietary intake in this sample is consistent with body measures that indicate excessive weight in about a quarter of students studied.
In the current study, physical fitness was assessed by the Queen’s step test, which estimates VO2max. Categories of physical fitness as determined by VO2max have been established by sex and age . Higher numbers for VO2max indicate better physical fitness. Overall, mean VO2max placed the student sample in the “good” category for physical fitness level at baseline. Sixty-two percent of students had optimal physical fitness, which differs from the survey findings in which 90% of students reported engaging in the recommended 150 min/week of moderate to vigorous physical activity. Overreporting may explain the difference between the survey results and the fitness test results.
As with most health behavior studies, self-selection bias of the convenience sample was a limitation in this study. Students who are interested in health are motivated to participate and may be more likely to improve health habits than others. The additional research experience in the intervention group could also have contributed to reporting bias between treatment groups.
Another major limitation involved exposure to feedback and promotion of health goals in the control group after the initial diet and physical activity surveys. Future studies could include a waitlist control or manipulate the initial survey to exclude feedback and goal selection.
Anthropometric measurements may be subject to some amount of error. For example, menstrual cycles may affect body measurements. We did not account for these potential confounding variables during the study.
Reported intake is known to be biased, with underreporting directly associated with BMI [, ]. ALIVE used food frequency questionnaires to identify intake. Food frequency questionnaires are more biased and less correlated than food diaries [ ]. Actual measurement of food intake is costly and time consuming. This study included the novel method of exploring the use of objectively measured partial dietary intake by measuring snack intake at study visits.
The fact that participants could choose very different goals (ie, increasing fruit/vegetable intake, decreasing fat/sugar intake, or increasing physical activity) and could switch tracks during the program indicated that we did not have sufficient power. Goal switching as a limitation has been addressed in ALIVE-PD, a future version of ALIVE, which is currently being evaluated for efficacy in adults. In ALIVE-PD, users work on diet and physical activity goals simultaneously, rather than switching between goals. Larger studies using ALIVE-PD in college students are warranted to document changes in diet and physical activity.
ALIVE is feasible in a diverse college population, as demonstrated by high participation and retention. Furthermore, health behaviors of college students, such as reducing saturated fat intake and increasing fruit intake can be influenced by ALIVE. Large randomized control trials using adaptations of ALIVE are warranted.
This project was supported by the University of Maryland College Park Designated Research Initiative Fund and by UL1RR031988 from the National Institute of Health, National Center for Advancing Translational Sciences. Study size was manageable thanks to the following volunteer undergraduate research assistants from the University of Maryland: Kyra Cappelucci, Tralisa Colby, Olivia Didion, Lisa Kaufman, Lisa Le, Caroline Keys (nee Pugh), Lisa Le, Derek Lipton, Haaris Majid, Michelle Ofori, Renita Tyler-Richards, Patricia Sanabria, and Pamela Winter. We thank Dr Carolyn Voorhees for the loan of the stadiometer and Dr Larry Douglas for his assistance with SAS programming and statistical analysis.
AS, CK, EM, and KL conceived the study. AS and JR conducted the study visits. AS analyzed the data and was the primary author.
Conflicts of Interest
- Anderson DA, Shapiro JR, Lundgren JD. The freshman year of college as a critical period for weight gain: An initial evaluation. Eat Behav 2003 Nov;4(4):363-367. [CrossRef] [Medline]
- Levitsky DA, Halbmaier CA, Mrdjenovic G. The freshman weight gain: A model for the study of the epidemic of obesity. Int J Obes Relat Metab Disord 2004 Nov;28(11):1435-1442. [CrossRef] [Medline]
- Gillen MM, Lefkowitz ES. The 'freshman 15': Trends and predictors in a sample of multiethnic men and women. Eat Behav 2011 Dec;12(4):261-266 [FREE Full text] [CrossRef] [Medline]
- Kann L, Kinchen S, Shanklin SL, Flint KH, Kawkins J, Harris WA, Centers for Disease ControlPrevention (CDC). Youth risk behavior surveillance--United States, 2013. MMWR Surveill Summ 2014 Jun 13;63 Suppl 4:1-168 [FREE Full text] [Medline]
- Juonala M, Raitakari M, S A Viikari Jorma, Raitakari OT. Obesity in youth is not an independent predictor of carotid IMT in adulthood. The Cardiovascular Risk in Young Finns Study. Atherosclerosis 2006 Apr;185(2):388-393. [CrossRef] [Medline]
- Freedman DS, Mei Z, Srinivasan SR, Berenson GS, Dietz WH. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: The Bogalusa Heart Study. J Pediatr 2007 Jan;150(1):12-17.e2. [CrossRef] [Medline]
- Gordon-Larsen P, The NS, Adair LS. Longitudinal trends in obesity in the United States from adolescence to the third decade of life. Obesity (Silver Spring) 2010 Sep;18(9):1801-1804 [FREE Full text] [CrossRef] [Medline]
- Sun SS, Liang R, Huang TT, Daniels SR, Arslanian S, Liu K, et al. Childhood obesity predicts adult metabolic syndrome: The Fels Longitudinal Study. J Pediatr 2008 Feb;152(2):191-200 [FREE Full text] [CrossRef] [Medline]
- Butler SM, Black DR, Blue CL, Gretebeck RJ. Change in diet, physical activity, and body weight in female college freshman. Am J Health Behav 2004;28(1):24-32. [Medline]
- Kasparek DG, Corwin SJ, Valois RF, Sargent RG, Morris RL. Selected health behaviors that influence college freshman weight change. J Am Coll Health 2008;56(4):437-444. [CrossRef] [Medline]
- Nelson MC, Larson NI, Barr-Anderson D, Neumark-Sztainer D, Story M. Disparities in dietary intake, meal patterning, and home food environments among young adult nonstudents and 2- and 4-year college students. Am J Public Health 2009 Jul;99(7):1216-1219 [FREE Full text] [CrossRef] [Medline]
- Nelson MC, Story M. Food environments in university dorms: 20,000 calories per dorm room and counting. Am J Prev Med 2009 Jun;36(6):523-526. [CrossRef] [Medline]
- Mackey E, Schweitzer A, Hurtado ME, Hathway J, DiPietro L, Lei KY, et al. The feasibility of an e-mail-delivered intervention to improve nutrition and physical activity behaviors in African American college students. J Am Coll Health 2015;63(2):109-117. [CrossRef] [Medline]
- National College Health Assessment II: Reference Group Executive Summary Spring 2013. Hanover, MD: American College Health Association; 2013. URL: http://www.acha-ncha.org/docs/ACHA-NCHA-II_ReferenceGroup_ExecutiveSUmmary_Spring2013.pdf [accessed 2016-01-19] [WebCite Cache]
- Merchant G, Weibel N, Patrick K, Fowler JH, Norman GJ, Gupta A, et al. Click “like” to change your behavior: A mixed methods study of college students' exposure to and engagement with Facebook content designed for weight loss. J Med Internet Res 2014;16(6):e158 [FREE Full text] [CrossRef] [Medline]
- Park A, Nitzke S, Kritsch K, Kattelmann K, White A, Boeckner L, et al. Internet-based interventions have potential to affect short-term mediators and indicators of dietary behavior of young adults. J Nutr Educ Behav 2008;40(5):288-297. [CrossRef] [Medline]
- Patrick K, Marshall SJ, Davila EP, Kolodziejczyk JK, Fowler JH, Calfas KJ, et al. Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). Contemp Clin Trials 2014 Jan;37(1):10-18 [FREE Full text] [CrossRef] [Medline]
- Cavallo DN, Tate DF, Ries AV, Brown JD, DeVellis RF, Ammerman AS. A social media-based physical activity intervention: A randomized controlled trial. Am J Prev Med 2012 Nov;43(5):527-532 [FREE Full text] [CrossRef] [Medline]
- Hager R, George JD, LeCheminant JD, Bailey BW, Vincent WJ. Evaluation of a university general education health and wellness course delivered by lecture or online. Am J Health Promot 2012;26(5):263-269. [CrossRef] [Medline]
- Harvey-Berino J, Pope L, Gold BC, Leonard H, Belliveau C. Undergrad and overweight: An online behavioral weight management program for college students. J Nutr Educ Behav 2012;44(6):604-608. [CrossRef] [Medline]
- Hebden L, Cook A, van der Ploeg H P, King L, Bauman A, Allman-Farinelli M. A mobile health intervention for weight management among young adults: A pilot randomised controlled trial. J Hum Nutr Diet 2014 Aug;27(4):322-332. [CrossRef] [Medline]
- Huang S, Hung W, Chang M, Chang J. The effect of an internet-based, stage-matched message intervention on young Taiwanese women's physical activity. J Health Commun 2009;14(3):210-227. [CrossRef] [Medline]
- Magoc D, Tomaka J, Bridges-Arzaga A. Using the web to increase physical activity in college students. Am J Health Behav 2011;35(2):142-154. [Medline]
- Napolitano MA, Hayes S, Bennett GG, Ives AK, Foster GD. Using Facebook and text messaging to deliver a weight loss program to college students. Obesity 2013 Jan 21;21(1):25-31. [CrossRef] [Medline]
- Gow RW, Trace SE, Mazzeo SE. Preventing weight gain in first year college students: An online intervention to prevent the “freshman fifteen”. Eat Behav 2010 Jan;11(1):33-39 [FREE Full text] [CrossRef] [Medline]
- Greene GW, White AA, Hoerr SL, Lohse B, Schembre SM, Riebe D, et al. Impact of an online healthful eating and physical activity program for college students. Am J Health Promot 2012;27(2):e47-e58. [CrossRef] [Medline]
- Levitsky DA, Garay J, Nausbaum M, Neighbors L, Dellavalle DM. Monitoring weight daily blocks the freshman weight gain: A model for combating the epidemic of obesity. Int J Obes (Lond) 2006 Jun;30(6):1003-1010. [CrossRef] [Medline]
- Kattelmann KK, Bredbenner CB, White AA, Greene GW, Hoerr SL, Kidd T, et al. The effects of Young Adults Eating and Active for Health (YEAH): a theory-based Web-delivered intervention. J Nutr Educ Behav 2014;46(6):S27-S41. [CrossRef] [Medline]
- Elderidge S, Kerry S. A Practical Guide to Cluster Randomised Trials in Health Services Research. West Sussex, United Kingdom: John Wiley and Sons Ltd; 2012:1-267.
- CDC. National Health and Nutrition Examination Survey Anthropometry Procedures Manual. Atlanta, GA: CDC; 2007 Jan 31. URL: http://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf [accessed 2016-02-08] [WebCite Cache]
- Ben-Noun LL, Laor A. Relationship between changes in neck circumference and cardiovascular risk factors. Exp Clin Cardiol 2006;11(1):14-20 [FREE Full text] [Medline]
- Lohman TG. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books; 1988.
- Block G, Hartman AM, Naughton D. A reduced dietary questionnaire: Development and validation. Epidemiology 1990 Jan;1(1):58-64. [Medline]
- Ainsworth BE, Irwin ML, Addy CL, Whitt MC, Stolarczyk LM. Moderate physical activity patterns of minority women: The Cross-Cultural Activity Participation Study. J Womens Health Gend Based Med 1999;8(6):805-813. [CrossRef] [Medline]
- Lalonde I, Ghram M, Slovinec-D'Angelo M, Beaton L, Brown J, Block T. Validation of the Block Fat/Sugar/Fruit/Vegetable Screener in a cardiac rehabilitation setting. Poster #9. J Cardiopulm Rehabil Prev 2008 Oct;28(5):340. [CrossRef]
- Physical Activity Guidelines Advisory Committee. U.S. Department of Health and Human Services. Washington, DC: U.S. Department of Health and Human Services; 2008. Physical Activity Guidelines for Americans URL: http://health.gov/paguidelines/pdf/paguide.pdf [accessed 2016-01-20] [WebCite Cache]
- McArdle W, Katch F, Katch V. Exercise Physiology: Energy, Nutrition, and Human Performance. Philadelphia, PA: Lippincott Williams & Wilkins; 2009.
- Sternfeld B, Block C, Quesenberry CP, Block TJ, Husson G, Norris JC, et al. Improving diet and physical activity with ALIVE: A worksite randomized trial. Am J Prev Med 2009 Jun;36(6):475-483. [CrossRef] [Medline]
- Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009 Apr;42(2):377-381 [FREE Full text] [CrossRef] [Medline]
- Heywood V. Advanced Fitness Assessment and Exercise Prescription. Champaign, IL: Human Kinetics; 2006.
- Arnold TJ, Schweitzer A, Hoffman HJ, Onyewu C, Hurtado ME, Hoffman EP, et al. Neck and waist circumference biomarkers of cardiovascular risk in a cohort of predominantly African-American college students: A preliminary study. J Acad Nutr Diet 2014 Jan;114(1):107-116 [FREE Full text] [CrossRef] [Medline]
- Almosnino S, Pelland L, Stevenson JM. Retest reliability of force-time variables of neck muscles under isometric conditions. J Athl Train 2010;45(5):453-458 [FREE Full text] [CrossRef] [Medline]
- Ben-Noun L, Sohar E, Laor A. Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes Res 2001 Aug;9(8):470-477. [CrossRef] [Medline]
- Raman A, Fitch MD, Hudes ML, Lustig RH, Murray CB, Ikeda JP, et al. Baseline correlates of insulin resistance in inner city high-BMI African-American children. Obesity (Silver Spring) 2008 Sep;16(9):2039-2045. [CrossRef] [Medline]
- Jung C, Fischer N, Fritzenwanger M, Figulla HR. Anthropometric indices as predictors of the metabolic syndrome and its components in adolescents. Pediatr Int 2010 Jun;52(3):402-409. [CrossRef] [Medline]
- Schwandt P, Bertsch T, Haas G. Anthropometric screening for silent cardiovascular risk factors in adolescents: The PEP Family Heart Study. Atherosclerosis 2010 Aug;211(2):667-671. [CrossRef] [Medline]
- U.S. Department of Agriculture, U.S. Department of Health Human Services. Dietary Guidelines for Americans. Washington, DC: Government Printing Office; Dec 2010.
- Bingham SA, Gill C, Welch A, Day K, Cassidy A, Khaw KT, et al. Comparison of dietary assessment methods in nutritional epidemiology: Weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records. Br J Nutr 1994 Oct;72(4):619-643. [Medline]
- Illner A, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol 2012 Aug;41(4):1187-1203 [FREE Full text] [CrossRef] [Medline]
|ALIVE: A Lifestyle Intervention Via Email|
|BIA: bioelectrical impedance analysis|
|BMI: body mass index|
|CCAPQ: Cross-Cultural Activity Patterns Questionnaire|
|DBP: diastolic blood pressure|
|DGA: Dietary Guidelines for Americans|
|eHealth: electronic health interventions|
|FM%: body fat mass percent|
|IRB: Institutional Review Board|
|NC: neck circumference|
|REDCap: Research Electronic Data Capture|
|SBP: systolic blood pressure|
|VO2max: maximum oxygen utilization|
|WC: waist circumference|
|WHR: waist-to-hip ratio|
Edited by P Bamidis; submitted 26.06.15; peer-reviewed by D Kapoukranidou, C Mouzakidis, G Merchant; comments to author 20.07.15; revised version received 14.09.15; accepted 13.11.15; published 29.02.16Copyright
©Amy L Schweitzer, Jamisha T Ross, Catherine J Klein, Kai Y Lei, Eleanor R Mackey. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.02.2016.
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