Appetite 173 (2022) 105979
Contents lists available at ScienceDirect
Appetite
journal homepage: www.elsevier.com/locate/appet
A pilot study examining the impact of a brief health education intervention
on food choices and exercise in a Latinx college student sample
Julie Blow a, Roberto Sagaribay III b, Theodore V. Cooper b, *
a
b
Texas Tech University Health Sciences Center El Paso, 5001 El Paso Drive, El Paso, TX, 79905, USA
The University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, USA
A R T I C L E I N F O
A B S T R A C T
Keywords:
Transtheoretical model
Self-determination theory
Intervention
Eating
Exercise
Self-monitoring
Healthy eating and physical activity (PA) necessitate interventions designed to increase these behaviors. SelfDetermination Theory (SDT) posits addressing psychological needs to promote intrinsic motivation, while the
Transtheoretical Model (TTM) posits progression through stages of change consistent with contemplating pro­
cesses of change. Previous findings suggest the efficacy of combining these approaches to understand, initiate,
and maintain behavior. This study assessed a pilot intervention to increase healthy eating and PA based on
components derived from SDT and TTM. Latinx college students (N = 267) were randomized to either the Fit U
intervention or the self-monitoring only group. The Fit U intervention augmented self-monitoring with
personalized, culturally-tailored motivational enhancement feedback and goal setting. Inferential analyses used
hierarchical regression models to predict total calorie intake, fruit and vegetable (FV) intake, eating behavior,
PA, and perceived competence for diet and exercise. Logistic regression models were used to examine changes in
motivation to engage in a healthy diet and PA at post-test. Findings suggest those in Fit U reported lower calorie
intake (β = 0.143, p = .023), improvement in healthy eating (β = − 0.157, p < .001), increased perceived
competence for diet (β = − 0.145, p = .007) and exercise (β = − 0.167, p = .003), and progression through the
stages of change for exercise (OR = 0.297, p = .003). Findings suggest the efficacy of personalized, culturallytailored motivational enhancement and goal setting beyond simply self-monitoring on healthy eating and PA
outcomes in Latinx college students. Future directions include assessing the impact of Fit U on a larger scale and
including long term follow-up assessments to assess the sustainability of eating and PA changes and their impact
on superordinate outcomes such as weight loss.
1. Introduction
In the United States, 42.4% of adults are obese (Center for Disease
Control and Prevention [Centers for Disease Control and Prevention,
2020). Obesity, defined as a body mass index (BMI) of 30 or greater, is
associated with coronary heart disease, Type 2 diabetes, certain cancers,
hypertension, stroke, osteoarthritis, and high cholesterol (Centers for
Disease Control and Prevention, 2020). Obesity prevalence in Hispanic
individuals (39.7%) is disproportionately higher than their White,
Non-Hispanic and Asian counterparts (39.7% vs 32.2% and 12.5%), as
are obesity-related diseases (Centers for Disease Control and Prevention,
2020). Hispanic health and health disparities are often shaped by factors
such as lack of access to preventative care (i.e., health care, safe spaces
for exercise, and healthy foods) and language/cultural barriers (U.S.
Department of Health and Human Services, 2021). Compared to
Non-Hispanic Whites, Hispanics often have higher availability of fresh
foods and a family-oriented meal pattern that serves as a protective
factor against unhealthy eating (Skala Dortch et al., 2012). However,
studies in the U.S. have suggested more acculturated Hispanic families
eat fewer FV, less rice, drink more soda, and eat more fast food
compared to less acculturated families (Creighton et al., 2012). Addi­
tionally, Hispanic families are more likely to experience food insecurity
(Coleman-Jenson et al., 2021) which is associated with obesity,
emotional eating, and poorer diets (Lopez-Cepero et al., 2020; Sharkey
et al., 2017). The perception often reported by Hispanic individuals of
regular PA includes language and family obligations as barriers and not
considering PA as a health-promoting behavior (Joseph et al., 2018).
Instead, PA is considered a luxurious and selfish activity. Despite these
* Corresponding author. Prevention and Treatment of Clinical Health Laboratory Department of Psychology University of Texas at El Paso, 500 West University
Avenue, El Paso, TX, 79968, USA.
E-mail address: tvcooper@utep.edu (T.V. Cooper).
https://doi.org/10.1016/j.appet.2022.105979
Received 1 April 2021; Received in revised form 23 February 2022; Accepted 24 February 2022
Available online 1 March 2022
0195-6663/© 2022 Elsevier Ltd. All rights reserved.
J. Blow et al.
Appetite 173 (2022) 105979
greater risks for poor health, few programs have been developed,
implemented, and assessed to enhance healthy eating and physical ac­
tivity in Latinx individuals; even fewer have done so within Latinx col­
lege students, the fasted growing ethnocultural minority group entering
college (U.S. Census Bureau, 2020).
Healthy eating (Centers for Disease Control and Prevention, 2020)
and physical activity (Fiuza-Luces et al., 2018) are the primary recom­
mendations to reduce morbidity and mortality associated with weight
such as the prevention of chronic diseases like high blood pressure, heart
disease, and Type 2 diabetes. Dietary guidelines suggest four servings of
fruits, five servings of vegetables per day (American Heart Association,
2017), and 150 min of moderate intensity physical activity (PA) per
week (United States Department of Health & Human Services, 2019).
Only 4.3% of college students report meeting guidelines for FV intake,
and approximately 45% report engaging in recommended levels of PA
(American College Health Association, 2019). Similar findings were
observed in a study of college students on the U.S./Mexico border (Hu
et al., 2011). Just 2% of students met guidelines for FV intake; however,
63% met PA guidelines. While PA findings are promising, healthy eating
may be lacking; thus, it is vital to encourage students to adopt healthier
eating behaviors and maintain, and even grow, regular physical activity.
Two theoretical models were utilized to conceptualize the develop­
ment of a Latinx college student healthy eating and PA intervention:
Self-Determination Theory (SDT; Ryan & Deci, 2000) and the Trans­
theoretical Model (TTM; Prochaska & Velicer, 1997). SDT is a
motivation-based model, purporting successful behavior change when
one moves from lacking motivation, to extrinsically motivated, to
intrinsically motivated. SDT posits increases in autonomy, competence,
and relatedness elicit internally motivated change (Ryan & Deci, 2000).
Many studies have used SDT-based weight loss, PA, and dietary behavior
interventions with promising results, as these increased autonomous
self-regulation, intrinsic motivation, and perceived competence relative
to non-theoretical interventions (Silva et al., 2010; Trief et al., 2017).
Specifically, interventions grounded in SDT suggest autonomous and
intrinsic motivation as predictors for improvements in food choices,
adherence to vigorous PA (Hartmann et al., 2015), and FV intake
(Kaponen et al., 2019). SDT-based interventions for Latinx samples are
limited, suggesting the need for assessments of novel interventions.
However, one study demonstrated both cultural beliefs (i.e., destiny
beliefs and familism) and SDT motivations predicted adherence to a
health information text messaging intervention (Cameron et al., 2017),
suggesting interventions targeting Latinx individuals should emphasize
personal control and one’s ability to change.
TTM is also a motivation-based model that is used to explain and
predict how and when behavior change occurs (Prochaska & Velicer,
1997). The theory posits behavior change involves progression through
different stages (i.e., precontemplation, contemplation, preparation,
action, and maintenance); multiple processes of change such as con­
sciousness raising, helping relationships, self-reevaluation, and rein­
forcement management are suggested to increase readiness through the
different stages (Prochaska & Velicer, 1997). Thus, identifying an in­
dividual’s stage of change (SOC) is beneficial in determining how to
intervene and the optimal processes of change (Prochaska & Velicer,
1997). TTM-based weight management interventions have demon­
strated improved body perception, reduced weight and body mass index,
and lower consumption of calories and foods high in fat (Johnson et al.,
2013; de Menezes et al., 2015). Similarly, SOC techniques (i.e.,
perceived competence, self-esteem) are also related to motivational
readiness to increase PA and improve nutrition (Boff et al., 2018; de
Menezes et al., 2016). While interventions for improving PA and healthy
eating are promising, little is known of the efficacy of TTM-based in­
terventions for diverse populations (i.e., Latinx samples). Thus, incor­
porating TTM in eating and activity based interventions and integrating
culturally tailoring with Latinx college students represents an innovative
approach, largely unaddressed in previous studies.
Intervention studies of SDT and TTM applied to weight related
changes have demonstrated efficacy, yet in some instances, it appears
TTM has yielded strong results in terms of healthy eating, and SDT has
demonstrated targeted PA outcomes. For example, an intervention
grounded in TTM demonstrated those in the action phase reported an
increase in FV intake and reductions in weight compared to those in
other stages of change; however, improvement in exercise was not as
successful (Karupaiah et al., 2015). Yet a recent study indicated an
intervention grounded in SDT increased autonomous motivation for
exercise (Donnachie et al., 2017). Given findings such as these, the
combining of these two models to promote both healthy eating and
regular PA may be optimal. In fact, one earlier study demonstrated Af­
rican American women were more likely to report less self-determined
forms of exercise regulation in the earlier stages and more
self-determined forms at the action and maintenance stages of TTM
(Landry & Solmon, 2004). One more recent study of 100 university
employees assessed a combined TTM/SDT group coaching intervention
to enhance PA. Results indicated that after 15 weeks, participants re­
ported higher scores on multiple processes of change and self-efficacy, as
well as multiple indicators of physical fitness (Bezner et al., 2020). Thus
Latinx-focused weight related assessments, intervention studies high­
lighting the efficacy of each theoretical model on either healthy eating
or PA, and combined intervention studies demonstrating desired out­
comes suggest the potential impact of combining these models to tailor a
healthy eating and physical activity intervention in Latinx college
students.
With regard to the content and modality of intervention strategies,
weight management interventions in young adults that included selfmonitoring and daily weighing aided in sustained weight loss (Carter
et al., 2017; Goldstein et al., 2019; Patel et al., 2020), and improved
adherence when accompanied with feedback (Burke et al., 2012;
Hutchesson et al., 2016). Moreover, many studies have demonstrated
the effectiveness of personalized feedback (i.e., dietary and physical
activity recommendations) to intervention adherence and positive out­
comes (Beleigoli et al., 2020). Culturally tailored interventions related
to diet and PA promotion in Hispanics have also yielded positive results
(McCurley et al., 2017; Prado et al., 2020). Some have included digital
or online feedback with mixed results (Chambliss et al., 2011; Johnson
et al., 2008), yet one study also noted a preference for interventions
offered on campus as opposed to online or other physical locations
(Gokee LaRose et al., 2011). However, gaps within the literature exist
such that many of these studies were conducted with overweight in­
dividuals (Gokee LaRose et al., 2010), with older individuals (Johnson
et al., 2008), and with limited Hispanic/Latinx representation (Johnson
et al., 2008). Taken together, the development and assessment of a
campus-based face to face intervention that augments self-monitoring
with highly personalized, motivationally based, culturally tailored
feedback and goal setting is innovative and warranted.
The aims of the current study were to assess the efficacy of a healthy
eating and PA intervention (Fit U) for Latinx college students. Hypoth­
eses were that the Fit U would demonstrate positive changes in primary
outcomes (i.e. total calorie intake, FV intake, eating behavior, and PA)
and secondary outcomes (i.e. motivation and competence to engage in a
healthy diet and PA) relative to a self-monitoring group. As the pilot
study was short, weight loss was not assessed.
2. Methods
2.1. Participants
A power analysis for a multiple linear regression (Cohen et al., 2003)
assuming 15% of variability in control variables, 2.5% for condition, and
power set to 0.95 yielded a necessary sample size of 267 participants.
Latinx college students (N = 267) were recruited from university psy­
chology courses. Eighty-eight percent were retained at post-test,
resulting in a sample size of 235 (68% female; see Fig. 1 for Flow of
Participation). Individuals of Latinx descent and/or report being
2
J. Blow et al.
Appetite 173 (2022) 105979
2.2.5. Stage of change (5 a day) for FV consumption
Assessed number of FV servings consumed per day (Vallis et al.,
2003) and evaluated SOC (fewer than five servings = precontemplation,
contemplation, preparation; five or more servings = action or
maintenance).
2.2.6. wt decisional balance (WDB)
A 20-item measure assessed positive and negative aspects of losing
weight (O’Connell & Velicer, 1988). Responses are summed to create
pros and cons. Higher scores indicate greater endorsement of positive
and negative aspects of losing weight. High internal consistency was
observed in the current study (α = 0.92 and α = 0.83, respectively).
2.2.7. Eating behavior inventory (EBI)
A 26-item measure assessed weight loss and management behaviors
(O’Neil et al., 1979). Items are summed; higher scores indicate behav­
iors conducive to weight loss. The internal reliability for the EBI was
0.67.
2.2.8. Food and activity log
Participants were instructed to record the brand, description, and
serving size of each food into a paper food and activity log. PA type and
duration was also recorded. Total calorie intake was derived from par­
ticipants’ logs and calculated by study staff using CalorieKing.com
(CalorieKingWellness Solutions, 2013). This database derives nutri­
tional content from trusted sources and is checked by dieticians. FV
intake was calculated by serving sizes, and PA was calculated as number
of minutes.
2.2.9. Body composition analyzer
Participants’ height, weight, and body composition were measured
in person without shoes by a body composition analyzer (Tanita Body
Composition Analyzer - Model TBF-215).
2.2.10. Total energy expenditure (TDEE)
TDEE, or daily calorie needs, were calculated using the HarrisBenedict Equation. This equation is commonly used to estimate Basal
metabolic rate (BMR) based on the height, weight, sex, and age of the
individual and then multiplies the derived value by an activity factor to
obtain and individual’s TDEE (Harris & Benedict, 1919). BMR was ob­
tained from the body composition analyzer’s output.
Fig. 1. Flow of participation.
international/Mexican comprise 83% of the student body; females
represent approximately 54% of undergraduates (UTEP Center for
Institutional Evaluation, Research and Planning, 2020), suggesting the
representativeness of the present sample (though the sample is skewed
female).
2.3. Procedure
The study was approved by the Institutional Review Board from the
University of Texas at El Paso and was in accordance with the Helsinki
Declaration of 1975, as revised in 2000. Students initiated appointments
through a secure online database responding to a description of the
eligibility criteria, which were posted online and assessed in person by
researchers at the appointment time. Inclusion criteria were: 1) aged 18
or older and 2) self-report Latinx ethnicity. Exclusion criteria were: 1)
currently pregnant or nursing and 2) currently participating in a formal
diet and/or exercise program. During the initial appointment, the re­
searchers first conducted an eligibility screening interview by asking
participants their age and ethnicity. Ineligible students were thanked
and issued partial course credit. Eligible participants completed the
informed consent and baseline assessments. After, participants were
randomized into the self-monitoring or the Fit U group using an online
random number generator. The randomization process was included in
the informed consent. Interventionists were trained and supervised by
the PI and provided both conditions. Each interventionist completed
worksheets using participants’ responses in order to ensure fidelity.
Baseline sessions lasted approximately 2 h to include consent, assess­
ments, body composition analysis, and intervention delivery. The first
weekly check in lasted under an hour; timing was based largely on goal
attainment, questions, and the extent to which new goals needed to be
2.2. Measures
2.2.1. Sociodemographics
Demographic information and risks associated with weight, such as
family/personal history of Type 2 diabetes, high blood pressure, heart
disease, and high cholesterol, were obtained through self-report.
2.2.2. Perceived competence scale diet (PCSD)
A 4-item measure assessed confidence in ability to maintain a
healthy diet (Deci & Ryan, 1985). Scores are averaged; higher scores
indicate greater perceived competence for diet. Internal reliability for
the PCSD was .93.
2.2.3. Perceived competence scale exercise (PCSE)
This measure is similar in scoring, number of items, and interpreta­
tion to the PCSD, but assessed confidence in ability to exercise regularly
(Deci & Ryan, 1985). Internal reliability for the PCSE was .92.
2.2.4. Exercise stage of change: short form (ESOC)
A single item measure assessed whether participants were engaged in
or plan to engage in regular exercise. Responses determine SOC (Marcus
et al., 1992).
3
J. Blow et al.
Appetite 173 (2022) 105979
developed. The final session also lasted under an hour and included posttest assessment completion, body composition analysis, and debriefing.
college students, within the present sample, doing so seemed more a
necessity in order to increase activity. In addition, students noted that
exercising alone seemed selfish and noted collectivistic themes
regarding physical activity participation. Through highlighting benefits
and reducing barriers, each participant was able to initiate a tailored
activity plan to enhance activity in a manner consistent with work, ac­
ademic, and family obligations (Please see Supplementary Table 1 for
intervention mapping of culturally tailored examples and themes for
healthy eating and physical activity enhancement).
Goals were recorded for diet and exercise for the upcoming week.
Additional generic “Tips” handouts adapted from a manual for main­
taining a healthy diet and PA (Cooper & Burke, 2003) were reviewed
with the participant, and these were personalized and tailored based
upon their unique responses to intervention components (Please see
Supplementary Material 2 for intervention worksheets and initial “Tips”
handouts prior to individualized tailoring). Participants were given the
same instruction in completing food and activity logs as the
self-monitoring group. Goal attainment was assessed one week later at
the first check-in. New goals or the continuation of current goals were
outlined.
A week after the first check-in, all participants had body composition
measured, completed post-test assessments and were debriefed. Partic­
ipants were awarded 5 h of course credit.
2.3.1. Self-monitoring group
Participants had body composition measured after completing the
baseline survey to avoid affecting responses. Results were provided at
the study’s completion. Participants were given instruction in
completing food and activity logs, which included accurately recording
various food servings (i.e. “a serving of meat is about the size and
thickness of a deck of playing cards”), the manner in which the food was
prepared (i.e. breaded and fried, or grilled), and minutes engaged in PA.
Participants were asked to record their food and physical activity intake
for a period of two weeks.
2.3.2. Fit U intervention
The Fit U group was provided with body composition feedback to
provide optimal data to them when taking part in other intervention
components, such as goal setting. Motivation to eat a healthy diet was
assessed, guided by participants’ baseline survey responses. A decisional
balance exercise determined personal positive and negative aspects of a
healthy diet; benefits and barriers were assessed. Decisional balance and
motivational enhancement are consistent with both TTM processes such
as self-reevaluation (Prochaska & Velicer, 1997) and promoting auton­
omy and competence within SDT (Ryan & Deci, 2000). Interventionists
helped participants consider components that contributed to the scale
tipping in favor of maintaining a healthy diet. While traditional reasons
for eating healthily were noted and emphasized (e.g., better health,
control weight), culturally specific factors were also highlighted such as
enhancing healthy family eating. As part of the strategies to overcome
any barriers to maintaining a healthy diet, the interventionist elicited
from the participant what it means to “eat healthy” and assisted in
debunking any ideas that food should be boring or bland in order to be
considered healthy. By using foods that the participant enjoys, this ac­
tivity utilized culturally-relevant food items, as has been found to be
efficacious in previous interventions (Foreyt et al., 1991). As an exercise,
favorite food items that are typically viewed as unhealthy were decon­
structed and reconstructed into a healthier version of that food. The
participant was encouraged to make a list of different ways that various
foods can be made healthier with a few small changes, such as utilizing
low-calorie and nutritionally dense condiments, such as salsa, in place of
high fat options like cheese or sour cream. This exercise also was used to
address other noted, often culturally-related barriers, for example that
often students are not the primary cooks in their household and thus do
not have control over how the food is prepared, yet do have control over
portion size, plate assembly, and condiments. There was also a strong
noted desire to not offend the family member who cooks by refusing
food; interventionists suggested polite refusal strategies yet also high­
lighted refusal of foods is unnecessary and emphasized learning to build
a nutritionally better balanced plate.
A decisional balance exercise and motivation and barriers to PA were
assessed similarly. Interventionists helped participants consider com­
ponents that contribute to the scale being tipped in favor of exercising
regularly. Note that throughout these activities, theoretical constructs
were also integrated when possible (e.g., TTM stimulus control, SDT
relatedness). Again, in addition to common benefits of physical activity
increases (e.g., enhanced health), more culturally tailored strategies
were emphasized such as exercising with family members and using this
time to provide and receive social support. These strategies were often
utilized to overcome noted barriers such as lack of time to exercise due
to family, home, and work commitments. That physical activity needs to
be structured and intense was debunked in favor of incorporating PA
into needed daily activities (e.g., parking further and walking greater
distance, taking the stairs). Further, that many students were com­
muters, and some lived across the border in Mexico, led to participants
reporting safety concerns, as well as financial impediments to activity.
Thus, while using University facilities for activity is an option for many
2.4. Statistical analyses
Hypotheses were specified before the data were collected. All base­
line missing data were imputed prior to analyses using the hot deck
imputation method (Roth, 1994). The variables used were sex, student
classification, and annual income. Responses from participants who had
complete data and who matched the participant with missing values on
the aforementioned variables were used to impute missing values
(Myers, 2011). Missing data analyses for the current dataset found that
0.29% of the values were missing.
A logistic regression model was used to assess baseline differences
between study completers and non-completers. Four hierarchical mul­
tiple linear regressions assessed differences between groups across pri­
mary outcome variables: total calorie intake, FV intake, PA, and healthy
eating behaviors at post-test. Multicollinearity was not observed in any
model. Independent variables were entered in stepwise, in which Step 1
control variables were entered (i.e., age, sex, BMI, and interventionist)
and in Step 2 group condition (i.e. self-monitoring or Fit U) was entered.
Two hierarchical multiple linear regressions assessed differences be­
tween groups across changes in perceived competence for diet and ex­
ercise at post-test. For these analyses, control variables were entered in
Step 1 (i.e., age, sex, BMI, interventionist, baseline scores on the pros
and cons scales of the WDB, and baseline scores from the PCSD or PCSE),
in Step 2 condition was entered, and in Step 3 the pros and cons of losing
weight at follow up were entered. Logistic regression analyses assessed
motivational changes for FV intake and exercise. Change was concep­
tualized as “forward movement” or “no forward movement” between
baseline and post-test assessment. Independent variables were entered
in stepwise, in which Step 1 control variables were entered (i.e., age, sex,
BMI, interventionist, and baseline WDB pros and cons), in Step 2 con­
dition was entered, in Step 3 post-test WDB pros and cons were entered,
and in Step 4 the interaction of post-test WDB pros and cons by condition
were entered.
3. Results
Frequencies of sociodemographic variables, eating behaviors, and
physical activity are reported in Table 1. Baseline differences between
study completers and non-completers were marginally significant, χ2
(14) = 23.64, p = .051, Nagelkerke R2 = 0.17. Completers were more
likely to report more minutes of weekly PA at baseline (OR = 1.01, p =
.004).
4
J. Blow et al.
Appetite 173 (2022) 105979
Table 1
Participant characteristics (Nbaseline = 267; Npost-test = 235).
Characteristic
Mean
Age
20.70
Sex
Female
Male
Classification
Freshman
Sophomore
Junior
Senior
Graduate
Weight
Baseline
Males
173.22
Females
136.47
Post-test
Males
171.33
Females
137.64
BMI
Baseline
Males
25.69
Females
23.98
Post-test
Males
25.49
Females
24.11
Smoking status
Daily 5 < 10
Daily <5
Weekly
Monthly
No longer smoke, in past smoked at least 1
per day
No longer smoke, in past smoked weekly
Experimented with cigarettes
Never smoked
Self-reported healthy eating and physical activity
Strength training (days per week)
2.16
Cardiovascular exercise (minutes per
255.78
week)
Daily fruit and vegetable intake (cup
2.16
servings)
Observed healthy eating and physical activity at post-test
Daily calorie intake
1735.60
Cardiovascular exercise (minutes per
195.20
week)
Daily fruit and vegetable intake (cup
.84
servings)
Type 2 diabetes history
Personal
Yes
Family
Yes
Heart disease history
Personal
Yes
Family
Yes
High cholesterol history
Personal
Yes
Family
Yes
High blood pressure history
Personal
Yes
Family
Yes
SDT
Baseline
PCS D (range 1–7)
4.85
PCS E (range 1–7)
5.58
Post-test
PCS D (range 1–7)
4.89
PCS E (range 1–7)
5.41
SD
Table 1 (continued )
Characteristic
Frequency
(%)
TTM
ESC Baseline
Precontemplation
Contemplation
Preparation
Action
Maintenance
ESC Post-test
Precontemplation
Contemplation
Preparation
Action
Maintenance
5 A Day SoC Baseline
Precontemplation
Contemplation
Preparation
Action
Maintenance
5 A Day SoC Post-test
Precontemplation
Contemplation
Preparation
Action
Maintenance
Baseline
WDB Pros (range 10–50)
WDB Cons (range 10–50)
Post-test
WDB Pros (range 10–50)
WDB Cons (range 10–50)
Eating Behavior
Baseline
EBI (range 26–130)
Post-test
EBI (range 26–130)
4.42
68.2
31.8
55.1
27.7
13.1
3.4
.7
39.11
26.43
40.67
29.48
5.07
4.32
5.25
4.41
.4
1.9
3.8
5.3
4.2
2.3
42.4
39.7
1.99
265.39
Mean
SD
Frequency
(%)
1.5
11.6
32.2
25.5
29.2
1.3
13.7
10.3
47.9
26.9
11.1
41.1
40.0
2.3
5.3
10.9
45.0
44.0
4.1
4.9
32.87
25.63
10.24
7.65
33.49
27.33
11.55
8.16
72.18
9.78
75.07
10.92
When assessing caloric intake, sex (β = − 0.367, p < .001) and group
condition (β = 0.143, p = .023) were statistically significant such that
females and those in the Fit U condition reported less caloric intake (see
Table 2). The models assessing FV intake and PA were not statistically
significant. In the model assessing eating behaviors, sex (β = 0.105, p =
.023), EBI scores at baseline (β = 0.709, p < .001), and group condition
(β = − 0.157, p < .001) were significant predictors such that females,
higher EBI scores, and Fit U participants reported higher EBI scores at
post-test.
Significant predictors of increased perceived competence for diet at
post-test included: higher PCS D baseline scores (β = 0.565, p < .001),
higher WDB cons at baseline (β = 0.234, p = .006), the Fit U condition (β
= − 0.145, p = .007), and lower WDB cons at follow-up (β = − 0.364, p
< .001). When the interaction of post-test WDB pros and cons by con­
dition were entered, the overall model was significant but incremental
variance was not (see Table 2) Fit U participants reported greater
perceived competence for diet at post-test relative to self-monitoring
participants.
Significant predictors of increased perceived competence for exercise
at post-test included: higher PCS E baseline scores (β = 0.613, p < .001),
higher WDB cons baseline scores (β = 0.301, p = .001), the Fit U con­
dition (β = − 0.167, p = .003), higher WDB pros scores at post-test (β =
0.250, p = .006), and lower WDB cons scores at post-test (β = − 0.285, p
= .001). When the interaction of post-test WDB pros and cons by con­
dition were entered, the overall model was significant but incremental
variance was not (see Table 2). Fit U participants reported greater
perceived competence for exercise at post-test relative to selfmonitoring participants.
Changes in motivation for increasing FV intake were assessed using
the SOC (5 A Day). Greater likelihood of forward movement to increase
FV intake was associated with female sex (OR = 2.731, p = .021), lesser
1.37
530.46
253.89
.85
0
43.8
.4
18.7
2.2
39.3
1.9
56.9
1.36
1.23
1.26
1.32
5
J. Blow et al.
Appetite 173 (2022) 105979
Table 2
Summary of the hierarchical regressions predicting average calorie intake,
eating behavior, perceived competence for diet, and perceived competence for
exercise at post-test.
Variable
Average Calorie
Intake
Eating Behavior
Perceived
Competence
for Diet
Step 1
Age
Sex
BMI
Interventionist B
Interventionist C
Interventionist D
R2
Step 2
Age
Sex
BMI
Interventionist B
Interventionist C
Interventionist D
Condition
R2
ΔR2
Step 1
Age
Sex
BMI
EBI Baseline
Interventionist B
Interventionist C
Interventionist D
R2
Step 2
Age
Sex
BMI
EBI Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
R2
ΔR2
Step 1
Age
Sex
BMI
PCS D Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
R2
Step 2
Age
Sex
BMI
PCS D Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
R2
ΔR2
Step 3
Age
Sex
BMI
PCS D Baseline
B
SE B
β
− 1.745
− 410.985
− 10.082
43.858
− 83.064
− 105.815
7.619
72.843
7.417
87.531
87.747
95.569
-.015
-.355**
-.089
.034
-.064
-.075
− 2.363
− 425.571
− 7.526
− 96.328
37.018
− 75.275
151.358
7.553
72.454
7.433
86.778
87.006
94.781
66.265
-.020
-.367**
-.067
-.068
.029
-.058
.143*
.018
2.171
.197
.769
-.619
− 1.119
-.277
.112
1.107
.109
.051
1.289
1.278
1.471
.008
.092
.086
.706**
-.024
-.043
-.009
.031
2.470
.143
.772
-.507
− 1.393
-.650
− 3.429
.110
1.083
.107
.050
1.257
1.249
1.438
.965
.013
.105*
.062
.709**
-.021
-.019
-.053
-.157**
.014
-.152
-.005
.489
.023
.016
.159
.017
.053
.008
.050
-.055
-.018
.523**
.189*
-.008
.009
-.052
.073
.259
.400
.180
.178
.202
.024
.086
.114*
.014
-.103
-.009
.511
.021
.015
.159
.017
.053
.008
.052
-.038
-.035
.546**
.177*
-.010
.009
-.063
.091
.231
.361
-.347
.178
.177
.200
.139
.030
.076
.103
-.137*
.015
-.157
-.017
.528
.015
.152
.016
.052
.054
-.057
-.063
.565**
Table 2 (continued )
.135**
.155**
.020*
.541**
Perceived
Competence
for Exercise
.563**
.024**
.382**
.399**
.017*
Variable
B
SE B
β
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
WDB Pros Posttest
WDB Cons Posttest
R2
ΔR2
Step 4
Age
Sex
BMI
PCS D Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
WDB Pros Posttest
WDB Cons Posttest
WDB Pros Posttest by Condition
WDB Cons Posttest by Condition
R2
ΔR2
.002
.012
.014
.038
.014
.234*
.092
.179
.368
-.368
.020
.170
.171
.191
.135
.010
.030
.059
.105
-.145*
.183*
-.056
.012
-.364**
Step 1
Age
Sex
BMI
PCS E Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
R2
Step 2
Age
Sex
BMI
PCS E Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
R2
ΔR2
Step 3
Age
Sex
BMI
PCS E Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
.456**
.057**
.015
-.151
-.016
.526
.000
.015
.153
.016
.052
.012
.053
-.055
-.061
.563**
-.001
.039
.014
.238*
.087
.181
.369
-.548
.011
.171
.171
.192
.555
.018
.028
.060
.105
-.217
.100
-.054
.027
-.350*
.007
.012
.126
-.002
.016
-.030
.457**
.001
-.014
-.194
.017
.612
.007
.017
.173
.018
.061
.008
-.050
-.068
.060
.566**
.054
.013
.010
.078
-.088
.209
.369
.192
.190
.217
-.028
.066
.100
-.013
-.121
.011
.638
.006
.016
.171
.018
.060
.008
-.046
-.042
.039
.589**
.044
.010
.010
.060
-.071
.180
.324
-.453
.189
.187
.214
.147
-.022
.057
.088
-.171*
-.012
-.156
.005
.664
-.021
.016
.166
.017
.059
.012
-.041
-.054
.017
.613**
-.168
.051
.015
.301*
-.066
.183
.348**
.375**
.027*
-.020
(continued on next page)
6
J. Blow et al.
Appetite 173 (2022) 105979
vegetables used as toppings, condiments, or ingredients toward total
servings (i.e. fruit in yogurt parfaits, vegetables in sandwiches, and fruits
or vegetables in smoothies). Even with this methodology, post-test logs
yielded an average of less than one cup per day for the sample. It should
be noted that while the intervention focused on improving healthy
eating, due to the highly tailored nature of the intervention, it seemed
that participants were more concerned with respect for familial and
cultural expectations and did not focus on healthy eating as increasing
fruit and vegetable intake. Given the benefits derived from consuming
the recommended amounts of fruits and vegetables daily (Toh et al.,
2020; Wang et al., 2021), it is imperative to refine the current inter­
vention in order to improve fruit and vegetable intake in this group.
Given that feedback regarding daily calorie needs was efficacious in
reducing overall calorie intake in the Fit U condition, perhaps a similar
health education component that outlines recommended daily servings
of fruits and vegetables should be incorporated into future iterations.
One previous study found that awareness of recommended daily serv­
ings of fruits and vegetables was associated with a greater likelihood of
consuming the recommended amount (Erinosho et al., 2012). Moreover,
efficacy may be further bolstered by eliciting strategies to incorporate
more fruits and vegetables into participants’ current diets. For example,
adding fruit to oatmeal or cereal at breakfast or vegetables to sand­
wiches at lunch can assist in achieving daily recommended amounts of
fruits and vegetables.
Table 2 (continued )
Variable
B
SE B
β
Interventionist C
Interventionist D
Condition
WDB Pros Posttest
WDB Cons Posttest
R2
ΔR2
Step 4
Age
Sex
BMI
PCS E Baseline
WDB Pros
Baseline
WDB Cons
Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
WDB Pros Posttest
WDB Cons Posttest
WDB Pros Posttest by Condition
WDB Cons Posttest by Condition
R2
ΔR2
.114
.342
-.441
.028
.182
.207
.145
.010
.036
.093
-.167*
.250*
-.046
.013
-.285*
.420**
.045**
-.012
-.153
.005
.659
-.023
.016
.167
.017
.060
.013
-.042
-.053
.019
.609**
-.181
.051
.015
.304*
-.070
.116
.341
-.739
.019
.184
.183
.208
.601
.019
-.022
-.037
.093
-.279
.167
-.050
.029
-.311
.007
.013
.122
.002
.018
.028
4.3. Physical activity
Increased PA was also not associated with Fit U. Weekly PA, both
self-reported at baseline and derived from logs at post-test, were wellabove the recommended amount for the sample (United States Depart­
ment of Health & Human Services, 2019). Hu et al. (2011) also observed
high rates of exercise in a similar sample. A ceiling effect may account
for the lack of change in PA from baseline to post-test. Analyses indi­
cated those lost to post-test reported fewer minutes of baseline PA per
week, suggesting those who remained were exercising the most. Future
studies may wish to focus retention efforts on those who report lower
levels of PA initially.
.421**
.001
Note: * all values significant at the 0.05 level.
** all values significant at the 0.001 level.
endorsement of the pros of weight loss at baseline (OR = 0.949, p =
.009), Interventionist C (OR = 2.725, p = .022), and Interventionist D
(OR = 3.012, p = .025). In later steps, no other additional variables were
significant (see Table 3).
Changes in PA motivation were assessed using the ESOC. Increased
likelihood of forward movement in ESOC was associated with Fit U (OR
= 0.297, p = .003) and higher post-test WDB pros (OR = 1.135, p =
.010). In later steps, no other additional variables were significant (see
Table 3). Fit U participants reported greater PA motivation relative to
self-monitoring participants.
Data (Blow & Cooper, 2020) are available through fig­
share:10.6084/m9.figshare.12730712.
4.4. Eating behavior
That females were more likely to report improvement in healthy
eating behaviors at post-test suggests females may be more amenable to
making healthy eating changes relative to their male counterparts. One
possible explanation may be that females in Latinx cultures contribute
more to food purchase and preparation (McVey et al., 2020) and were
thus more sensitive to the nuances of healthy eating processed through
Fit U relative to males. As predicted, improvement in healthy eating
behaviors was associated with Fit U, indicating the EBI was more sen­
sitive to capturing such changes. Rather than focusing on fruit and
vegetable intake, the EBI focuses on behaviors such as regulating the
quantity of food or the place that food is consumed (at the table versus
while reading, or watching TV), and this mirrors aspects of the Fit U
intervention. Cultural emphases on addressing food quantity, healthy
alternatives for enjoyed recipes (e.g., use of corn tortillas), and using
time away from home to eat healthily with greater portion control may
have contributed to the efficacy of Fit U in healthy eating. It is also
promising that general healthy eating behavior change occurred, as one
recent study found that improvement in one area increases the odds of
improving in other areas (Johnson et al., 2013), though these effects
were observed over longer follow-up periods. Perhaps changes in eating
behavior may act as a catalyst to changes in fruit and vegetable intake
and physical activity over time.
4. Discussion
4.1. Calorie intake
Consistent with hypotheses, those in Fit U reported lower calorie
intake relative to those in the self-monitoring group. Though restricting
calories was not instructed, perhaps those in Fit U made healthier
choices due to the highly personalized and culturally sensitive feedback
(i.e., portion control, healthier substitutions in recipes, and daily calorie
needs) and the motivational enhancement exercise. These findings show
promise, as previous studies suggest even a small calorie deficit can be
beneficial (Romeijn et al., 2020). Interventions with longer follow-ups
are needed to assess whether these changes are maintained.
4.2. Fruit and vegetable intake
Increased FV intake was not associated with Fit U participation. In
line with suggestions from Hu et al. (2011) and the USDA (2020, pp.
2020–2025), researchers in the current study counted items such as
salsas, agua frescas, and fruit and vegetable juices, as well as fruits and
4.5. Perceived competence for diet
Increased perceived competence for diet at post-test was associated
7
J. Blow et al.
Appetite 173 (2022) 105979
Table 3
Summary of the Logistic Regressions Predicting 5 A day Stage of Change Movement and Exercise Stage of Change Movement.
Variables
B
SE B
Odds Ratio
Confidence Interval (CI)
p
5 A Day Stage of Change Movement
Step 1
Age
.032
.035
1.033
.964–1.107
.360
Sex
1.005
.435
2.731
1.165–6.403
.021
BMI
.042
.043
1.043
.958–1.135
.333
WDB Pros Baseline
-.052
.020
.949
.913–.987
.009
WDB Cons Baseline
.010
.024
.1.011
.964–1.059
.661
Interventionist B
.392
.449
1.479
.614–3.564
.383
Interventionist C
1.002
.438
2.725
1.155–6.427
.022
Interventionist D
1.103
.492
3.012
1.147–7.907
.025
Step 2
Age
.032
.035
1.033
.963–1.107
.364
Sex
.993
.437
2.699
1.147–6.351
.023
BMI
.043
.044
1.044
.959–1.137
.318
WDB Pros Baseline
-.052
.020
.949
.913–.987
.009
WDB Cons Baseline
.012
.024
1.012
.965–1.061
.627
Interventionist B
.390
.449
1.477
.613–3.559
.385
Interventionist C
1.014
.439
2.757
1.165–6.525
.021
Interventionist D
1.119
.495
3.061
1.160–8.076
.024
Condition
.123
.342
1.131
.579–2.209
.718
Step 3
Age
.037
.036
1.307
.967–1.112
.304
Sex
1.047
.445
2.849
1.190–6.818
.019
BMI
.052
.045
1.054
.965–1.150
.245
WDB Pros Baseline
-.085
.030
.919
.866–.974
.005
WDB Cons Baseline
-.026
.037
.975
.907–1.047
.482
Interventionist B
.396
.457
3.115
.607–3.638
.385
Interventionist C
.916
.452
1.486
1.031–6.060
.043
Interventionist D
1.136
.504
2.500
1.161–8.356
.024
Condition
.314
.356
1.369
.681–2.754
.378
WDB Pros Post-test
.039
.024
1.040
.992–1.090
.106
WDB Cons Post-test
.055
.033
1.057
.990–1.129
.097
Step 4
Age
.036
.036
1.037
.967–1.112
.308
Sex
1.078
.450
2.938
1.216–7.099
.017
BMI
.055
.045
1.057
.967–1.155
.220
WDB Pros Baseline
-.093
.032
.911
.856–.969
.003
WDB Cons Baseline
-.022
.037
.979
.910–1.052
.560
Interventionist B
.372
.458
1.451
.591–3.564
.417
Interventionist C
.927
.453
2.528
1.041–6.139
.041
Interventionist D
1.148
.505
3.153
1.171–8.490
.023
Condition
-.131
1.622
.877
.037–21.051
.935
WDB Pros Post-test
.009
.044
1.009
.926–1.101
.833
WDB Cons Post-test
.070
.077
1.073
.922–1.248
.363
WDB Pros Post-test by Condition
.025
.032
1.026
.963–1.092
.427
WDB Cons Post-test by Condition
-.013
.046
.987
.902–1.081
.781
2
Note: Step 1 χ2 (8) = 17.174, p = .028, Nagelkerke R = .117; Step 2 χ2 (9) = 17.304, p = .044, Nagelkerke R2 = .118; Step 3 χ2 (11) = 24.109,
p = .012, Nagelkerke R2 = .162; Step 4 χ2 (13) = 24.773, p = .025, Nagelkerke R2 = .166
Exercise Stage of Change Movement
Step 1
Age
Sex
BMI
WDB Pros Baseline
WDB Cons Baseline
Interventionist B
Interventionist C
Interventionist D
Step 2
Age
Sex
BMI
WDB Pros Baseline
WDB Cons Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
Step 3
Age
Sex
BMI
WDB Pros Baseline
WDB Cons Baseline
Interventionist B
Interventionist C
-.024
-.123
-.002
.024
.006
-.195
-.323
.484
.035
.466
.045
.021
.025
.448
.478
.559
.976
.885
.998
1.024
1.006
.823
.724
1.622
.911–1.046
.355–2.207
.913–1.090
.983–1.067
.959–1.056
.342–1.978
.284–1.847
.542–4.854
.490
.793
.957
.251
.809
.663
.499
.387
-.024
.093
-.025
.028
-.002
-.253
-.465
.373
− 1.475
.037
.499
.049
.022
.026
.476
.513
.594
.389
.976
1.097
.975
1.028
.998
.776
.628
1.453
.229
.907–1.049
.413–2.919
.885–1.074
.984–1.074
.948–1.051
.306–1.972
.230–1.717
.453–4.657
.107–.490
.509
.852
.608
.216
.943
.595
.365
.530
.000
-.019
-.037
-.036
-.087
.057
-.241
-.636
.037
.517
.052
.049
.045
.490
.533
.981
.964
.964
.916
1.059
.786
.529
.912–1.055
.350–2.656
.871–1.067
.832–1.009
.970–1.156
.301–2.054
.186–1.506
.605
.943
.482
.076
.203
.623
.233
(continued on next page)
8
J. Blow et al.
Appetite 173 (2022) 105979
Table 3 (continued )
Variables
B
SE B
Odds Ratio
Confidence Interval (CI)
p
Interventionist D
Condition
WDB Pros Post-test
WDB Cons Post-test
Step 4
Age
Sex
BMI
WDB Pros Baseline
WDB Cons Baseline
Interventionist B
Interventionist C
Interventionist D
Condition
WDB Pros Post-test
WDB Cons Post-test
WDB Pros Post-test by Condition
WDB Cons Post-test by Condition
.443
− 1.215
.127
-.065
.615
.408
.050
.042
1.557
.297
1.135
.937
.467–5.195
.133–.660
1.030–1.251
.864–1.017
.471
.003
.010
.122
-.019
-.011
-.033
-.084
.056
-.204
-.601
.459
− 2.847
.106
-.139
.009
.047
.037
.527
.052
.049
.045
.493
.537
.624
1.998
.089
.090
.042
.050
.981
.989
.968
.919
1.057
.816
.548
1.582
.058
1.112
.870
1.009
1.048
.912–1.055
.352–2.776
.874–1.072
.834–1.013
.968–1.155
.311–2.143
.191–1.571
.465–5.378
.001–2.914
.934–1.324
.730–1.038
.929–1.096
.951–1.156
.612
.983
.529
.088
.218
.697
.263
.462
.154
.231
.122
.824
.343
Note: Step 1 χ2 (8) = 3.951, p = .862, Nagelkerke R2 = 0.039; Step 2 χ2 (9) = 19.560, p = .021, Nagelkerke R2 = 0.181; Step 3 χ2 (11) = 27.792, p = .003, Nagelkerke
R2 = 0.250; Step 4 χ2 (13) = 28.769, p = .007, Nagelkerke R2 = 0.258.
with Fit U. Similar findings were observed in a SDT-based intervention
relative to health education (Robbins et al., 2018). It appears processing
and overcoming healthy diet barriers in a culturally tailored manner (i.
e., focusing on respectful refusal and appreciation strategies with fam­
ily) bolstered perceived competence for diet and post-test EBI.
Increased perceived competence for diet at post-test was associated
with endorsing more cons of losing weight at baseline and fewer at posttest. Consistent with SDT (Ryan & Deci, 2000) enhancing dieting
self-efficacy appears to have reduced the number of negative aspects of
weight loss endorsed. Extending this finding to promoting weight loss
autonomy and utilizing a longer follow-up that permits measuring
weight loss is a necessary future direction.
for increasing FV intake, the intervention clearly was not efficacious in
moving individuals into the preparation or action stages. Adding
behavioral, action oriented strategies, above and beyond goal setting, to
the Fit U intervention may promote later stage movement. Also, Fit U
focused on general healthy eating behavior and did not specifically
target FV intake; thus, future iterations should be refined to bolster
motivation to increase FV intake.
4.8. Exercise stage of change movement
Movement through SOC for exercise was associated with Fit U.
Almost one-third of the sample was in preparation stage for exercise at
baseline. As previously mentioned, Fit U participants increased
perceived competence for exercise, which may have translated to
moving participants into the action stage. Studies in college student
(Ersoz & Eklund, 2016) and Mexican (Zamarripa et al., 2018) samples
have suggested more self-determined motivations and regulations (i.e.,
intrinsic) are associated with the later stages of change for exercise.
Many participants in Fit U reported time constraints as a barrier. Elic­
iting PA time management and coupling this strategy with culturally
relevant suggestions such as exercising with family may have bolstered
likelihood to engage, thereby enhancing motivation. Forward move­
ment through the stages for exercise was also associated with endorsing
the pros of weight loss at post-test. It may be increasing beliefs in the
benefits of losing weight enhances motivation to engage in behaviors,
such as PA.
4.6. Perceived competence for exercise
Consistent with hypotheses and research of SDT-based interventions
(Robbins et al., 2018), forward movement in PA perceived competence
was associated with Fit U. Discussing barriers and strategies was effi­
cacious in boosting perceived competence for exercise.Many of these
focused on integrating other family members into PA, while others
focused on minimizing discomfort with engaging in PA alone. Safety
concerns while engaging in PA alone in participants’ neighborhoods
were also cited as a barrier to PA. Interventionists explored feasibly
safety strategies with these participants. Other strategies focused on
garnering social support for PA from friends and involving them in
regularly scheduled PA in order to bolster consistency, which was
reportedly a barrier to PA. Increases at post-test were associated with
endorsing more cons of losing weight at baseline, fewer cons at post-test,
and endorsing more pros to weight loss at post-test. Promoting PA
competence appears to have reduced the negative aspects of weight loss
while increasing the positive aspects.
4.9. Strengths and limitations
Strengths of the study are the inclusion of normal-weight individuals,
minimal missing data and rates of attrition, utilizing an intervention
with theoretically derived components, the addressing and inclusion of a
traditionally underrepresented sample, and the use of a pilot trial that
can inform the development of larger scale interventions in Latinx col­
lege students. Additionally, the study contributes to the understanding
of SDT and TTM approaches with diverse samples and the effectiveness
of interventions grounded in them. More studies assessing SDT and TTM
components with Latinx and other ethnoculturally diverse samples are
warranted.
Limitations included using a convenience sample and short time to
post-test, which may have limited generalizability and removed the
capacity to assess weight changes. Self-reported data may have resulted
in inaccurate estimates of caloric, FV, and PA intake at baseline, yet this
is not likely to differ based on group nor distort changes over time. The
focus on general healthy eating in the intervention did not target FV
4.7. 5 A day stage of change movement
Forward movement through the SOC for increasing FV intake was
associated with identifying as female and reporting fewer pros of weight
loss at baseline. While completing the food log may have heightened
awareness of FV intake and increased motivation to increase intake, it
may not have translated to behavior change due to the short amount of
time between baseline and post-test assessments. Indeed, discussing
dietary benefits and barriers during the Fit U intervention did not yield
movement through SOC for increasing FV intake. Previous research has
suggested reporting fewer healthy eating behaviors at baseline is asso­
ciated with less forward movement with the stages of change (Brick
et al., 2019). Since 41.1% of the sample were in the contemplation stage
9
Appetite 173 (2022) 105979
J. Blow et al.
intake and perhaps did not prepare participants to make changes to FV
intake. The intervention should be refined and bolster its efficacy for
enhancing motivation to increase FV intake. Lastly, although the inter­
vention and control groups differed significantly on multiple outcomes,
investigating the unique role of culturally tailored feedback could not be
assessed. Future iterations should compare interventions that only differ
in the type of tailored feedback provided for the participants (i.e.,
culturally tailored versus general feedback) to identify the relative ef­
ficacy of cultural tailoring in Latinx groups. Unhealthy weight control
behaviors should also be assessed in future studies for potential threats
to participants’ health.
Prevention and Treatment in Clinical Health Lab at UTEP, United States,
particularly Erica Landrau-Cribbs, Nicole Kim, Dessaray Gorbett, and
Theo Adams.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.appet.2022.105979.
References
American College Health Association. (2019). National college health assessment spring
2008 reference group data report (abridged). The American College Health Association,
Journal of American College Health, 57, 477–488. https://doi.org/10.3200/
JACH.57.5.477-488
American Heart Association. (2017, June). Fruits and vegetables serving sizes. https
://www.heart.org/en/healthy-living/healthy-eating/add-color/fruits-and-veget
ables-serving-sizes.
Beleigoli, A., Andrade, A. Q., Diniz, M. D., & Ribeiro, A. L. (2020). Personalized webbased weight loss behavior change program with and without dietitian online
coaching for adults with overweight and obesity: Randomized controlled trial.
Journal of Medical Internet Research, 22(11). https://doi.org/10.2196/17494
Bezner, J. R., Franklin, K. A., Lloyd, L. K., & Crixell, S. H. (2020). Effect of group health
behaviour change coaching on psychosocial constructs associated with physical
activity among university employees. International Journal of Sport and Exercise
Psychology, 18(1), 93–107. https://doi.org/10.1080/1612197x.2018.1462232
Blow, J., & Cooper, T. V. (2020). Fit U data. Figshare. https://dx.doi.org/10.6084/m9.fig
share.12730712.
Boff, R., Dornelles, M. A., Feoli, A. M., Gustavo, A., & Oliveira, M. (2018).
Transtheoretical model for change in obese adolescents: Merc Randomized Clinical
trial. Journal of Health Psychology, 25(13–14), 2272–2285. https://doi.org/10.1177/
1359105318793189
Brick, L. A. D., Yang, S., Harlow, L. L., Redding, C. A., & Prochaska, J. O. (2019).
Longitudinal analysis of intervention effects on temptations and stages of change for
dietary fat using parallel process latent growth modeling. Journal of Health
Psychology, 24(5), 572–585. https://doi.org/10.1177/1359105316679723
Burke, L. E., Styn, M. A., Sereika, S. M., Conroy, M. B., Ye, L., Glanz, K., … Ewing, L. J.
(2012). Using mHealth technology to enhance self-monitoring for weight loss.
American Journal of Preventive Medicine, 43(1), 20–26. https://doi.org/10.1016/j.
amepre.2012.03.016
CalorieKing Wellness Solutions. (2013). Retrieved from http://www.calorieking.com/.
Cameron, L. D., Durazo, A., Ramírez, A. S., Corona, R., Ultreras, M., & Piva, S. (2017).
Cultural and linguistic adaptation of a healthy diet text message intervention for
Hispanic adults living in the United States. Journal of Health Communication, 22(3),
262–273. https://doi.org/10.1080/10810730.2016.1276985
Carter, M. C., Burley, V. J., & Cade, J. E. (2017). Weight loss associated with different
patterns of self-monitoring using the mobile phone app My Meal Mate. JMIR MHealth
and UHealth, 5(2). https://doi.org/10.2196/mhealth.4520
Centers for Disease Control and Prevention. (2020). Adult obesity prevalence maps. Centers
for Disease Control and Prevention. Retrieved October 28, 2021, from https://www.
cdc.gov/obesity/data/prevalence-maps.html.
Chambliss, H. O., Huber, R. C., Finley, C. E., McDoniel, S. O., Kitzman-Ulrich, H., &
Wilkinson, W. J. (2011). Computerized self-monitoring and technology-assisted
feedback for weight loss with and without an enhanced behavioral component.
Patient Education and Counseling, 85, 375–382. https://doi.org/10.1016/j.
pec.2010.12.024
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/
correlation analysis for behavioral sciences (3rd ed.). Hillsdale: Erlbaum.
Coleman-Jenson, A., Rabbitt, M. P., Gregory, C. A., & Singh, A. (2021). Household food
security in the United States in 2020. U.S. Department of Agriculture: Economic
Research Service. Retrieved February 10, 2022, from https://www.ers.usda.gov/pub
lications/pub-details/?pubid=102075.
Cooper, T. V., & Burke, R. (2003). Tobacco cessation clinic: Training manual. G.V. (Sonny)
Montgomery Veterans Affairs Medical Center.
Creighton, M. J., Goldman, N., Pebley, A. R., & Chung, C. Y. (2012). Durational and
generational differences in Mexican immigrant obesity: Is acculturation the
explanation? Social Science & Medicine, 75(2), 300–310. https://doi.org/10.1016/j.
socscimed.2012.03.013
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human
behavior. New York: Plenum.
Donnachie, C., Wyke, S., Mutrie, N., & Hunt, K. (2017). “It”s like a personal motivator
that you carried around wi’ you’: Utilising self-determination theory to understand
men’s experiences of using pedometers to increase physical activity in a weight
management programme. International Journal of Behavioral Nutrition and Physical
Activity, 14(1). https://doi.org/10.1186/s12966-017-0505-z
Erinosho, T. O., Moser, R. P., Oh, A. Y., Nebeling, L. C., & Yaroch, A. L. (2012).
Awareness of the Fruits and Veggies—more Matters campaign, knowledge of the
fruit and vegetable recommendation, and fruit and vegetable intake of adults in the
2007 Food Attitudes and Behaviors (FAB) Survey. Appetite, 59, 155–160. https://doi.
org/10.1016/j.appet.2012.04.010
Ersöz, G., & Eklund, R. C. (2016). Behavioral regulations and dispositional flow in
exercise among American college students relative to stages of change and gender.
4.10. Conclusions and future directions
Findings suggest the efficacy of a brief pilot intervention for Latinx
college students in reducing caloric intake, improving healthy eating,
increasing perceived competence for diet and exercise, and motivating
progression through SOC for exercise. Clinical implications include the
importance of motivation in promoting healthy eating, the use of
personalized and culturally tailored data from participants within the
intervention, and the importance of goal setting to optimize success.
Implications for future research include longer term follow-up assess­
ments, greater emphasis on fruit and vegetable intake, assessing the
efficacy of culturally tailored feedback relative to more generic feed­
back, and the retention of those reporting lower levels of initial physical
activity. Further, given Latinx college students’ high rates of smartphone
and social media use (Lerma et al., 2021), adapting self-monitoring and
intervention elements to digital platforms may promote heightened
participant convenience and greater reach. Finally such efforts should
also include the assessments of unhealthy eating behaviors and weight
loss goals and outcomes to ultimately promote Latinx college student
health.
Contributors
Blow: Conceptualization, Data curation, Formal analysis, Investi­
gation, Methodology, Project administration, Writing – original draft
Sagaribay: Writing –review and editing Cooper: Conceptualization,
Methodology, Investigation, Resources, Formal analysis, Writing – re­
view and editing, Supervision.
All authors significantly contributed to the writing and editing of the
manuscript and have given approval of submission of their work for
consideration of publication in Appetite.
Role of funding source
No specific grants were sought from funding agencies in the public,
commercial, and not-for-profit sectors for this research.
Ethical statement
The study was approved by the Institutional Review Board from the
University of Texas at El Paso and was in accordance with the Helsinki
Declaration of 1975, as revised in 2000. Eligible participants completed
the informed consent process before proceeding to the baseline
assessments.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The authors would like to acknowledge all members of the
10
J. Blow et al.
Appetite 173 (2022) 105979
Journal of American College Health, 65(2), 94–102. https://doi.org/10.1080/
07448481.2016.1239203
Fiuza-Luces, C., Santos-Lozano, A., Joyner, M., Carrera-Bastos, P., Picazo, O.,
Zugaza, J. L., Izquierdo, M., Ruilope, L. M., & Lucia, A. (2018). Exercise benefits in
cardiovascular disease: Beyond attenuation of traditional risk factors. Nature Reviews
Cardiology, 15(12), 731–743. https://doi.org/10.1038/s41569-018-0065-1
Foreyt, J. P., Ramirez, A. G., & Cousins, J. H. (1991). Cuidando El corazon- a weightreduction intervention for Mexican-Americans. American Journal of Clinical Nutrition,
53, 1639S-41S. Retrieved from http://www.ajcn.org/content/vol53/issue6/index.
dtl.
Gokee LaRose, J., Gorin, A. A., Clarke, M. M., & Wing, R. R. (2011). Beliefs about weight
gain among young adults. Potential challenges to prevention. Obesity, 19,
1901–1904. https://doi.org/10.1038/oby.2011.203
Gokee LaRose, J., Tate, D. F., Gorin, A. A., & Wing, R. R. (2010). Preventing weight gain
in young adults: A randomized controlled pilot study. American Journal of Preventive
Medicine, 39, 63–68. https://doi.org/10.1016/j.amepre.2010.03.011
Goldstein, S. P., Goldstein, C. M., Bond, D. S., Raynor, H. A., Wing, R. R., & Thomas, J. G.
(2019). Associations between self-monitoring and weight change in behavioral
weight loss interventions. Health Psychology, 38(12), 1128–1136. https://doi.org/
10.1037/hea0000800
Harris, J. A., & Benedict, F. G. (1919). A biometric study of basal metabolism in man.
Washington DC: Carnegie Institution.
Hartmann, C., Dohle, S., & Siegrist, M. (2015). A self-determination theory approach to
adults’ healthy body weight motivation: A longitudinal study focussing on food
choices and recreational physical activity. Psychology and Health, 30(8), 924–948.
https://doi.org/10.1080/08870446.2015.1006223
Hu, D., Taylor, T., Blow, J., & Cooper, T. V. (2011). Multiple health behaviors: Patterns
and correlates of diet and exercise in a Hispanic college sample. Eating Behaviors, 12,
296–301. https://doi.org/10.1016/j.eatbeh.2011.07.009
Hutchesson, M. J., Tan, C. Y., Morgan, P., Callister, R., & Collins, C. (2016). Enhancement
of self-monitoring in a web-based weight loss program by extra individualized
feedback and reminders: Randomized trial. Journal of Medical Internet Research, 18
(4), 82. https://doi.org/10.2196/jmir.4100
Johnson, S. S., Paiva, A. L., Cummins, C. O., Johnson, J. L., Dyment, S. J., Wright, J. A.,
Prochaska, J. O., Prochaska, J. M., & Sherman, K. (2008). Transtheoretical modelbased multiple behavior intervention for weight management: Effectiveness on a
population basis. Preventive Medicine, 46(3), 238–246. https://doi.org/10.1016/j.
ypmed.2007.09.010
Johnson, S. S., Paiva, A. L., Mauriello, L., Prochaska, J. O., Redding, C., & Velicer, W. F.
(2013). Coaction in multiple behavior change interventions: Consistency across
multiple studies on weight management and obesity prevention. Health Psychology.
https://doi.org/10.1037/a0034215
Joseph, N. M., Ramaswamy, P., & Wang, J. (2018). Cultural factors associated with
physical activity among U.S. adults: An integrative review. Applied Nursing Research,
42, 98–110. https://doi.org/10.1016/j.apnr.2018.06.006
Karupaiah, T., Wong, K., Chinna, K., Arasu, K., & Chee, W. S. (2015). Metering selfreported adherence to clinical outcomes in Malaysian patients with hypertension.
Health Education & Behavior, 42(3), 339–351. https://doi.org/10.1177/
1090198114558588
Koponen, A. M., Simonsen, N., & Suominen, S. (2019). How to promote fruits,
vegetables, and berries intake among patients with type 2 diabetes in primary care?
A self-determination theory perspective. Health Psychology Open, 6(1). https://doi.
org/10.1177/2055102919854977, 205510291985497.
Landry, J. B., & Solmon, M. A. (2004). African American Women’s self-determination
across the stages of change for exercise. Journal of Sport & Exercise Psychology, 26(3),
457–469. https://doi.org/10.1123/jsep.26.3.457
Lerma, M., Marquez, C., Sandoval, K., & Cooper, T. V. (2021). Psychosocial correlates of
excessive social media use in a Hispanic college sample. Cyberpsychology, Behavior,
and Social Networking, 24(11), 722–728. https://doi.org/10.1089/cyber.2020.0498
Lopez-Cepero, A., Frisard, C., Lemon, S., & Rosal, M. (2020). Emotional eating partially
mediates the relationship between food insecurity and obesity in Latina women
residing in the Northeast U.S. Current Developments in Nutrition, 4(Supplement_2).
https://doi.org/10.1093/cdn/nzaa043_081, 230–230.
Marcus, B. H., Selby, V. C., Niaura, R. S., & Rossi, J. S. (1992). Self-efficacy and the stages
of exercise behavior change. Research Quarterly for Exercise & Sport, 63, 60–66.
Retrieved from http://www.aahperd.org/rc/publications/rqes/.
McCurley, J. L., Fortmann, A. L., Gutierrez, A. P., Gonzalez, P., Euyoque, J., Clark, T.,
Preciado, J., Ahmad, A., Philis-Tsimikas, A., & Gallo, L. C. (2017). Pilot test of a
culturally appropriate diabetes prevention intervention for at-risk Latina women.
The Diabetes Educator, 43(6), 631–640. https://doi.org/10.1177/
0145721717738020
McVey, B. A., Lopez, R., & Padilla, B. I. (2020). Evidence-based approach to healthy food
choices for Hispanic women. Hispanic Health Care International, 19(1), 17–22.
https://doi.org/10.1177/1540415320921471
Menezes, M. C., Bedeschi, L. B., Santos, L. C., & Lopes, A. C. S. (2016). Interventions
directed at eating habits and physical activity using the transtheoretical model: A
systematic review. Nutricion Hospitalaria, 33(5). https://doi.org/10.20960/nh.586
Menezes, M. C., Mingoti, S. A., Cardoso, C. S., de Mendonça, R., & Lopes, A. C. (2015).
Intervention based on transtheoretical model promotes anthropometric and
nutritional improvements — a randomized controlled trial. Eating Behaviors, 17,
37–44. https://doi.org/10.1016/j.eatbeh.2014.12.007
Myers, T. A. (2011). Goodbye listwise deletion: Presenting hotdeck imputation as an easy
and effective tool for handling missing data. Communication Methods and Measures, 5,
297–310. https://doi.org/10.1080/19312458.2011.624490
O’Connell, D., & Velicer, W. F. (1988). A decisional balance measure for weight loss.
International Journal of the Addictions, 23, 729–750. https://doi.org/10.3109/
10826088809058836
O’Neil, P. M., Currey, H. S., Hirsch, A. A., Malcolm, R. J., Sexauer, J. D., Riddle, F. E., &
Taylor, C. I. (1979). Development and validation of the eating behavior inventory.
Journal of Behavioral Assessment, 1, 123–132. https://doi.org/10.1007/BF01322019
Patel, M. L., Brooks, T. L., & Bennett, G. G. (2020). Consistent self-monitoring in a
commercial app-based intervention for weight loss: Results from a randomized trial.
Journal of Behavioral Medicine. https://doi.org/10.1007/s10865-019-00091-8
Prado, G., Fernandez, A., St George, S. M., Lee, T. K., Lebron, C., Tapia, M. I.,
Velazquez, M. R., & Messiah, S. E. (2020). Results of a family-based intervention
promoting healthy weight strategies in overweight Hispanic adolescents and parents:
An RCT. American Journal of Preventive Medicine, 59(5), 658–668. https://doi.org/
10.1016/j.amepre.2020.06.010
Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior
change. American Journal of Health Promotion, 12, 38–48. Retrieved from http
://healthpromotionjournal.com.
Robbins, L. B., Ling, J., Clevenger, K., Voskuil, V. R., Wasilevich, E., Kerver, J. M.,
Kaciroti, N., & Pfeiffer, K. A. (2018). A school- and home-based intervention to
improve adolescents’ physical activity and healthy eating. The Journal of School
Nursing. https://doi.org/10.1177/1059840518791290, 105984051879129.
Romeijn, M. M., Kolen, A. M., Holthuijsen, D. D., Janssen, L., Schep, G., Leclercq, W. K.,
& van Dielen, F. M. (2020). Effectiveness of a low-calorie diet for liver volume
reduction prior to bariatric surgery: A systematic review. Obesity Surgery, 31(1),
350–356. https://doi.org/10.1007/s11695-020-05070-6
Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel
Psychology, 47, 537–560. https://doi.org/10.1111/j.1744-6570.1994.tb01736.x
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of
intrinsic motivation, social development, and well-being. American Psychologist, 55,
68–78. https://doi.org/10.1037/0003-066X.55.1.68
Sharkey, J. R., Nalty, C., Johnson, C. M., & Dean, W. R. (2017). Chapter 10 children’s
very low food security is associated with increased dietary intakes in energy, fat, and
added sugar among Mexican-origin children (6-11 y) in Texas Border Colonias. Food
Insecurity and Disease, 179–198. https://doi.org/10.1201/9781315365763-11
Silva, M. N., Vieira, P. N., Coutinho, S. R., Minderico, C. S., Matos, M. G., Sardinha, L. B.,
& Teixeira, P. J. (2010). Using self-determination theory to promote physical activity
and weight control: A randomized controlled trial in women. Journal of Behavioral
Medicine, 33, 110–122. https://doi.org/10.1007/s10865-009-9239-y
Skala Dortch, K., Chuang, R.-J., Evans, A., Hedberg, A.-M., Dave, J., & Sharma, S. (2012).
Ethnic differences in the home food environment and parental food practices among
families of low-income Hispanic and African-American preschoolers. Journal of
Immigrant and Minority Health, 14(6), 1014–1022. https://doi.org/10.1007/s10903012-9575-9
Toh, D. W., Koh, E. S., & Kim, J. E. (2020). Incorporating healthy dietary changes in
addition to an increase in fruit and vegetable intake further improves the status of
cardiovascular disease risk factors: A systematic review, meta-regression, and metaanalysis of randomized controlled trials. Nutrition Reviews, 78(7), 532–545. https://
doi.org/10.1093/nutrit/nuz104
Trief, P. M., Cibula, D., Delahanty, L. M., & Weinstock, R. S. (2017). Self-determination
theory and weight loss in a diabetes prevention program translation trial. Journal of
Behavioral Medicine, 40(3), 483–493. https://doi.org/10.1007/s10865-016-9816-9
United States Department of Agriculture. (2020). Dietary guidelines for Americans (pp.
2020–2025). Retrieved from https://www.dietaryguidelines.gov/sites/default/files
/2021-03/Dietary_Guidelines_for_Americans-2020-2025.pdf.
United States Department of Health & Human Services. (2019). Physical activity guidelines
for Americans. https://www.hhs.gov/fitness/be-active/physical-activity-guidelines
-for-americans/index.html.
United States Department of Health & Human Services. (2021). Profile: Hispanic/Latino
Americans. Hispanic/latino - the office of minority health. Retrieved October 28, 2021,
from https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=64.
U.S. Census Bureau. (2020). Hispanic heritage month 2020. The United States census
Bureau. Retrieved September 23, 2020, from https://www.census.gov/newsroom
/facts-for-features/2020/hispanic-heritage-month.html.
UTEP Center for Institutional Evaluation, Research, and Planning. (2020). Factbook.
Retrieved October 28, 2021, from https://www.utep.edu/planning/cierp/Files/
docs/past-fact-books1/UTEP%20Fact%20Book%202013-14.pdf.
Vallis, M., Ruggiero, L., Greene, G., Jones, H., Zinman, B., Rossi, S., Edwards, L.,
Rossi, J. S., & Prochaska, J. O. (2003). Stages of change for healthy eating in
diabetes: Relation to demographic, eating-related, health care utilization, and
psychosocial factors. Diabetes Care, 26, 1468–1474. https://doi.org/10.2337/
diacare.26.5.1468
Wang, D. D., Li, Y., Bhupathiraju, S. N., Rosner, B. A., Sun, Q., Giovannucci, E. L.,
Rimm, E. B., Manson, J. A. E., Willett, W. C., Stampfer, M. J., & Hu, F. B. (2021).
Fruit and vegetable intake and mortality. Circulation, 143(17), 1642–1654. https://
doi.org/10.1161/circulationaha.120.048996
Zamarripa, J., Castillo, I., Baños, R., Delgado, M., & Álvarez, O. (2018). Motivational
regulations across the stages of change for exercise in the general population of
Monterrey (Mexico). Frontiers in Psychology, 9. https://doi.org/10.3389/
fpsyg.2018.02368
11