The HOMBRE Trial: Comparing Two Innovative Approaches to Reduce Chronic Disease Risk Among Latino Men
Phase: NA
Conditions: Obesity, Metabolic Syndrome, Lifestyle Intervention, Pre Diabetes
Sponsor: Palo Alto Medical Foundation

Title: The HOMBRE Trial: Comparing Two Innovative Approaches to Reduce Chronic 
Disease Risk Among Latino Men 
 
NCT number: NCT03092960 
 
Document date: 2/23/2018 
 
 
 

 
1. 
Introduction 
Obesity is a major contributor to the leading causes of death among all men in the United States 
(US) (e.g., type 2 diabetes, cardiovascular disease),1‐3 while rates are the highest among Latino 
men (39%).4 Consequently, Latino men bear a disproportionate disease burden attributable to 
excess weight, namely higher incidence of type 2 diabetes and prevalence of major 
cardiovascular risk factors (e.g., metabolic syndrome, pre-diabetes) compared to non-Hispanic 
white men.3,5‐8  Latino men are part of the largest minority group in the US with Latinos 
representing 18% US population. Therefore, identifying effective and scalable interventions for 
prevention of obesity-related chronic disease is a critical public health priority.  
 
Previous studies have proven behavioral lifestyle interventions are effective for promoting 
modest, yet clinically significant weight loss and can delay or prevent the onset of diabetes 
among high-risk adults in community and primary care settings.9-12  For example, the Diabetes 
Prevention Program (DPP) trial demonstrated that an intensive lifestyle intervention targeting 
modest weight loss (7%)  and increased moderate-intensity physical activity (150 minutes per 
week) lowered type 2 diabetes incidence by 58% among high-risk adults, including Latinos.13 A 
critical gap in current evidence limiting widespread implementation and dissemination is that the 
majority of research has focused on non-Hispanic white women.14 According to a 2012 
systematic review, men represented only 22% of participants--and racial/ethnic minority men 
only 2%--in behavioral weight loss intervention trials in the US.14 There have been no studies to 
date focused on how best to translate evidence-based DPP interventions among Latino 
men.11,12,15-20 Pragmatic research on population-specific approaches is urgently needed to 
ensure that Latino men have equitable access to opportunities that enable them to manage 
obesity and prevent costly chronic diseases.  
 
Compared to women, men experience less societal pressure for weight loss and are less likely 
to attempt weight loss.21,22 Thus, while both men and women indicate a desire for flexible and 
individualized interventions,23-26 this may be particularly important for men.27-31 One strategy for 
individualizing evidence-based interventions is to offer participants choices for how to engage in 
the intervention. While we and others have tested the effectiveness of various delivery options 
for behavioral weight loss interventions including videos,32-34 online,33,35,36 and in-person group 
sessions,32-34 few randomized controlled trials (RCTs) have tested an intervention that offers a 
package of delivery options from which participants can choose. Additionally, men’s primary 
motivation for weight loss appears to be reducing chronic disease risk factors and avoiding 
adverse health outcomes.14,21,37-39 Because their motivations for losing weight are related to 
health, the primary care setting may appeal to men.  
 
This paper describes a new RCT comparing the effectiveness and potential for future 
implementation and dissemination of the HOMBRE (Hombres con Opciones para Mejorar su 
Bienestar y Reducir Enfermedades Crónicas; English translation: Men with options to improve 
well being and reduce chronic disease) intervention that offers a suite of delivery options and 
minimal intensity intervention. Findings from the trial will advance the understanding of weight 
management and diabetes prevention among Latino men, the largest racial/ethnic group in the 
US.   
 
2.   Methods 
2.1 Study Design 
In this comparative effectiveness trial (11/2016-10/2019), at-risk Latino adult men will be 
randomized to one of two arms: (1) HOMBRE, a behavioral lifestyle intervention tailored for 
Latino men, or (2) a minimal intensity intervention. The study utilizes the RE-AIM (Reach, 

Effectiveness, Adoption, Implementation, and Maintenance) framework40 to systematically 
assess the potential for implementation and dissemination over time. The specific aims are: 
 
Aim 1: Compare the effectiveness of HOMBRE with a minimal intensity intervention for 
sustaining clinically significant weight loss among overweight and obese Latino men at 18 
months.  
Hypothesis:  A significantly higher percentage of overweight and obese Latino men 
randomized to the HOMBRE intervention will maintain clinically significant weight loss (≥5% of 
baseline weight) compared with those randomized to the minimal intensity intervention. 
Research staff will objectively measure participant weights at baseline and 18 months.  
Exploratory Aim 1: Compare weight trajectories of Latino men randomized to HOMBRE 
and the minimal intensity intervention over 18 months and 24 months. Although participants will 
be followed until 18 months in the study, longitudinal weight data from the electronic health 
record (EHR) will be abstracted through 24 months post randomization. 
 
Aim 2: Compare the effectiveness of HOMBRE with a minimal intensity intervention for 
improving cardiometabolic risk factors, health behaviors, and psychosocial well-being among 
overweight and obese Latino men at 18 months. 
 
Hypothesis: Compared with Latino men randomized to the minimal intensity 
intervention, HOMBRE participants will demonstrate statistically significant greater 
improvements in cardiometabolic indicators (blood pressure, waist circumference), health 
behaviors (diet, physical activity, sedentary behavior), and psychosocial well-being (quality of 
life and depressive symptoms) at 18 months.  
 
Exploratory Aim 2: Examine potential moderators assessed at baseline (e.g., marital 
status, acculturation, health literacy) and theoretically-based mediators assessed at 6 and 12 
months (e.g., self-efficacy, social support) of the effectiveness of HOMBRE compared to the 
minimal intensity intervention.  
 
 
Aim 3: Examine the other attributes of RE-AIM (reach, adoption, implementation, and 
maintenance) of the two interventions using mixed methods to inform rapid dissemination and 
long-term sustainability among overweight and obese Latino men.   
 
Exploratory Aim 3: Among those randomized to HOMBRE, examine socio-
demographic determinants of modality choice and compare intervention engagement and 
effectiveness according to modality to inform future dissemination. 
 
2.2. Patient and Stakeholder Engagement 
This study features a patient-engaged approach leveraging a Latino Patient Advisory Board and 
physician advisors. The Latino Patient Advisory Board is a group of Latino patients that 
originally formed to help culturally adapt the DPP-Group Lifestyle Balance intervention that 
forms the basis of the HOMBRE approach. The Advisory Board met weekly for 12 weeks and 
then monthly for 4 months to complete the cultural adaptation. During subsequent meetings, 
contributions of the advisory board have included selection of a measure of acculturation 
appropriate for the local Latino population, pre-testing MyFitnessPal, a web/Smartphone 
application for dietary tracking as well as a fitness tracker selected for HOMBRE to ensure 
acceptability, and determining the language(s) for intervention delivery. As the study 
progresses, the Advisory board will meet at least quarterly to provide critical input into the 
design and implementation of the study. Stakeholders include medical directors and primary 
care physicians involved in Latino men’s care. Stakeholders were engaged during the 
adaptation process through in-depth interviews and will be engaged during study 
implementation through research team presentations seeking approval for patient recruitment. .  
 

2.2 Comparative Effectiveness Pragmatic RCT 
2.2.1. Eligibility Criteria 
The eligibility criteria are designed to yield a generalizable sample of at-risk men balanced with 
ensuring patient safety, intervention adherence, and study retention. We will enroll Latino men 
18 years or older who are at risk for weight-related cardiometabolic conditions such as diabetes 
and heart disease. For this study, at-risk is defined as having a Body Mass Index (BMI) ≥27 
kg/m2 and one or more cardiometabolic risk factors (high waist circumference, high triglycerides, 
high blood pressure, high fasting plasma glucose, or low high-density lipoprotein cholesterol) 
(Table 1).1,2 Patients with significant psychiatric (e.g., bipolar or psychotic disorder) or medical 
comorbidities (e.g., active cancer, organ failure) will be excluded. Additional exclusions are to 
protect participant safety, prevent loss to follow-up (e.g., planned relocation, limited lifespan), 
and prevent contamination of study arms (e.g., family/household member of another study 
participant). 
 
Table 1. Inclusion and exclusion criteria 
Inclusion criteria: 
 
Age (as of date of enrollment):  
o 
Lower age limit: 18 years 
o 
Upper age limit: NONE (only exclude for cause, e.g. disease and functional limitations, 
as detailed below) 
 
Race/ethnicity: Latino of any race  
 
Gender: male  
 
Body mass index: ≥27 kg/m2 
 
At least one cardiometabolic risk factor: 
 
Elevated waist circumference 
 
Elevated triglycerides 
 
Elevated blood pressure  
 
Elevated fasting plasma glucose 
 
Low high-density lipoprotein cholesterol 
 
Primary Care Provider approval of study contact 
 
Able and willing to enroll and provide informed consent, i.e., to meet the time and data collection 
requirements of the study, be randomized to one of two study arms, participate in follow-up for 
24 months, and authorize extraction of relevant information from the EHR 
 
Exclusion criteria: 
 
Medical exclusions: 
o 
Previous diagnosis of diabetes or diabetes diagnosed as a result of fasting blood 
glucose or hemoglobin A1c levels obtained through study screening; 
o 
Diagnosis of cancer (other than non-melanoma skin cancer) that is/was active or 
treated with radiation or chemotherapy within the past 2 years; 
o 
Inability to walk without the assistance of another person; 
o 
Severe medical co-morbidities that require aggressive treatment: e.g., stage 4 or 
greater renal disease, class III or greater heart failure, unstable coronary artery 
disease, liver or renal failure; 
o 
Diagnosis of a terminal illness and/or in hospice care;  
o 
Diagnosis of bipolar disorder or psychotic disorder within the last 2 years, or currently 
taking a mood stabilizer or antipsychotic medication 
o 
Initiation or change in type or dosing of antidepressant medications within 2 months 
prior to enrollment (The patient will be re-contacted for a later cohort once his/her 
regimen has been stable for at least 2 months unless the person declines to participate 
altogether.)  
o 
Have had or plan to undergo bariatric surgery during the study period 
 
Other exclusions: 
o 
Having no reliable telephone service 
o 
Plan to move out of the area during the study period 
o 
Family/household member of another study participant or of a study staff member 
o 
Investigator discretion for clinical safety or protocol adherence reasons 
 

2.2.2. Recruitment and Screening 
Patients will be recruited in four cohorts from internal medicine and family medicine departments 
at the Palo Alto Medical Foundation (PAMF), a large community-based multispecialty group 
practice in the San Francisco Bay Area. The recruitment and screening process will involve the 
following 5 steps; (1) leveraging the EHR to identify patients who are potentially eligible using 
the inclusion and exclusion criteria; (2) seeking approval from patients’ primary care provider 
(PCP) who can exclude patients due to medical or psychiatric concerns; (3) sending PCP-
approved patients a recruitment email or letter from the Principal Investigator introducing the 
study and inviting them to complete an initial brief screening online in Spanish or English with an 
option to opt out (patients can self-screen at this step); (4) contacting patients who do not opt 
via telephone to screen for eligibility; (5) scheduling eligible and interested patients for an in-
person baseline assessment for final determination of eligibility. Bilingual and bicultural research 
assistants will be available to conduct screening in Spanish or English. Prior to the baseline 
assessment, patients will be provided the opportunity to complete a self-administered survey 
online in Spanish or English. Patients can also complete the survey at the baseline visit. The 
baseline visit will include the written informed consent, standardized measures of height, weight, 
waist circumference, and blood pressure, and interviewer-administered assessments of dietary 
intake and physical activity. At the conclusion of the baseline visit, all participants will receive a 
digital scales, wearable fitness tracker, and instructions for setting up Myfitnesspal, a web- or 
Smartphone-based application for tracking diet and physical activity.  
 
2.2.3. Randomization and Blinding 
Latino men deemed fully eligible will be randomized in a 1:1 ratio to receive the HOMBRE 
intervention or a minimal intensity intervention (controln =212/arm). Men will be informed of their 
treatment assignment and invited to an orientation visit. We will apply a covariate-adaptive 
biased coin method that we have published41 and used successfully in several trials42,43 to 
achieve good marginal balance between treatments across the following baseline 
characteristics: clinic, age, BMI, level of acculturation assessed by the Short Acculturation Scale 
for Hispanics, and number of cardiometabolic risk factors.44,45  The dynamic block randomization 
algorithm of our method automatically ensures allocation concealment.  By design, treatment 
will be identifiable to participants and the lifestyle coach, but masking of the investigators, safety 
officer, outcome assessors, and data analyst will be enforced.  Regardless of random 
assignment, all participants will continue to receive usual medical care. 
 
2.2.4. Orientation Visit 
Following randomization, all participants will be scheduled for an orientation group session 
specific to their randomization arm. The orientation session will be led by a bilingual and 
bicultural lifestyle coach and a research assistant focused on the technology components 
(fitness tracker and Smartphone application). Participants in both arms will be encouraged to 
bring their partner and/or other family members to the orientation session with the purpose of 
increasing understanding and social support among family members as advocated by the Latino 
Patient Advisory Board in the cultural adaptation process. Group orientation sessions for both 
arms will feature a didactic component to provide information on the background and goals of 
the intervention and an interactive small group discussion component.  
 
The small group component for the HOMBRE intervention will engage participants in a 
discussion on the advantages and disadvantages of the three delivery options: self-directed via 
online videos, coach-facilitated via in-person groups, and coach-facilitated via online 
videoconferencing groups. Following the small group discussion, men randomized to the 
HOMBRE arm will select their preferred option using a structured handout that guides them 
through important decision domains including preferred level of coach and peer support, comfort 

with technology, and lifestyle factors (e.g., work and family schedule).  Men will be encouraged 
to discuss their choice with their partner and family if appropriate. The small group discussion 
component for the minimal intensity intervention will focus on identifying challenges and 
potential solutions to intervention adherence. At the conclusion of the orientation session, men 
in both arms will receive their intervention materials and any needed technology support.   
 
2.2.5 Interventions and Fidelity Assurance 
2.2.5.1 Theoretical Basis  
Both interventions are based on Social Cognitive Theory,46 which emphasizes a triadic, 
reciprocally deterministic relationship between the individual, environment, and behavior.  It 
recognizes that behavior change is a dynamic process that moves at variable speed through 
stages of readiness to change.  Positive outcome expectancies through realistic goal setting 
and guided action planning are associated with initiation of behavior change. Self-efficacy 
developed for specific behaviors (e.g., physical activity) predict establishment and maintenance 
of behavior change. Social Cognitive Theory suggests that self-efficacy is enhanced through 
social support and gradual mastery of self-regulation skills (e.g., goal setting, self-monitoring).47 
 
2.2.5.2 Self-monitoring  
Participants in both arms will receive a digital scale to self-monitor weight, a wearable fitness 
tracker to self-monitor physical activity, and will be asked to use MyFitnessPal web or 
Smartphone application to track dietary intake. In pre-testing the Latino Patient Advisory Board 
concluded the application had high acceptability based on the language, ease of entering 
cultural foods, opportunities for social networking, and availability both on the phone and 
personal computer. We will help participants to set up the activity monitor at the baseline visit to 
ensure that these data are automatically and wirelessly synced with the self-monitoring 
application (MyFitnessPal) with minimal participant action needed except for wearing the device. 
MyFitnessPal is a free web- and mobile-based (Android, iOS, and Windows compatible) 
tracking application available in Spanish and English. Self-monitored data collected through the 
MyFitnessPal application will be used by the lifestyle coach to provide individualized feedback 
and guidance tailored for Latino men. In addition, self-monitoring data (e.g., self-monitoring 
frequency) will be used to assess intervention participants’ engagement and adherence. 
 
2.2.5.3 Minimal Intensity Intervention 
Men randomized to the minimal intensity intervention group (control) will be offered a self-
directed lifestyle program over a 12-month period. The program is based on the Group Lifestyle 
Balance (GLB) intervention, a group-based adaptation of the original DPP intervention.15,16,48 
The GLB program’s feasibility and effectiveness in primary care, and other community settings, 
are well documented.15-19,49,50 The GLB is an approved curricula for application of the CDC 
Diabetes Prevention Recognition Program, including electronically delivered modalities.51 The 
goal of GLB is to promote 5-10% weight loss as recommended by obesity treatment guidelines 
52 and a minimum of 150 minutes per week of moderate-intensity physical activity (e.g., brisk 
walking) as recommended by the 2008 Physical Activity Guidelines for Americans.53 The GLB 
supports participants in making healthy lifestyle changes to improve weight and prevent 
diabetes using theoretically-based behavioral strategies such as self-monitoring, goal setting, 
action planning and problem solving.  Participants receive access to 12 program videos, 
available online, and written information to use in completing home-based, independent 
activities designed to help them problem-solve challenges they may face in losing weight and 
increasing physical activity (Table 2).  The videos are available in English, with Spanish 
subtitles. A trained PAMF health coach is available upon request to answer participants’ 
questions by telephone, email, mobile text messaging or secure messaging through PAMF’s 
secure patient portal known as My Health Online.  

 
 
2.2.5.4 HOMBRE Intervention 
Men randomized to the HOMBRE intervention group will be offered three intervention delivery 
options from which they can choose one.  All three options are based on the GLB program over 
a 12-month period. Similar to GLB, the HOMBRE intervention includes 12 weekly sessions 
during the intensive phase (Months 1-3) and 10 biweekly or monthly contacts during the 
maintenance phase (Months 4-12).  
 
The three delivery options are: 1) self-directed via online videos or DVD (referred to as self-
directed), 2) coach-facilitated via in-person groups (referred to as in-person), and 3) coach-
facilitated via online groups (referred to as videoconferencing). The primary difference across 
delivery options is how men engage in the weekly sessions during the intensive phase (Table 
2). In the self-directed option men will view 12 videos (15-30 minutes each) online or via DVD 
during the intensive phase. The in-person group option includes 12 coach-facilitated group 
sessions at the clinic for the intense intervention phase. The coach-facilitated via online group 
option is similar to the in-person option but the meetings will take place via cloud-based video 
conferencing called Bluejeans. Participants can join over the internet or with a free smartphone 
application. The only hardware required is a computer or smartphone with an internet 
connection and speaker/microphone functionality. Participants will attend the sessions at 
specified times, but can join from wherever is convenient.  All three options deliver the GLB, and 
encourage web/mobile-based self-monitoring using Myfitnesspal and the wearable activity 
tracker. Men will choose their delivery option in the group orientation visit as described above. 
The lifestyle coach will reach out to men who have achieved less than 2% loss of their baseline 
weight by four weeks to see if a different delivery option is better. Changing options is possible 
because all three approaches offer the same content.  
 
A secondary difference across delivery options is the level and type of coach feedback (Table 
3) during the intensive (months 1-3) and maintenance (months 4-12) phases. The self-directed 
approach has the lowest level of coach involvement. Men in this option will receive standardized 
weekly messages in Months 1-3 with reminders to watch the videos, use written materials, self-
monitor via the activity tracker and Myfitnesspal, and reach out to the coach with questions or 
requests for more individualized feedback. In Months 4-12, men in the self-directed option will 
receive biweekly, and then monthly, standardized messages that will include handouts on 
Table 2.  Intervention features of the minimal intensity intervention and the HOMBRE intervention (by modality) 
 
Options in HOMBRE 
Minimal intensity 
intervention control 
 
Self-directed* 
In-Person* 
Videoconferencing*  
12 weekly core 
Sessions  
Home, DVD or online 
(30 minutes/session) 
Clinic, in-person, coach-led 
groups 
(45-60 minutes/session)  
Remote, coach-led groups via 
video conferencing  
(45-60 minutes/session) 
Home, DVD or online 
Level of coach 
support 
Medium: Standardized 
messages  
High: Coach-led group, 
individualized feedback, 
standardized messages 
High: Coach-led group, 
individualized feedback, 
standardized messages 
Low: Coach available to 
answer participant questions  
Type of peer 
support  
None 
Group: In-person groups 
Group: Online groups 
None 
Use of 
technology 
High: Videos online or DVD; 
messages via self-monitoring 
app 
Medium: Messages via self-
monitoring app 
High: Video conferencing, 
messages via self-monitoring 
app 
Medium: Videos online or DVD, 
optional messages via self-
monitoring app 
Convenience 
High: Engage in intervention at 
self-determined time and place 
Low: Come into clinic at a time 
determined by group (several 
times offered) 
Medium: Join groups from any 
location at time determined by 
group (several times offered) 
High: Engage in intervention at 
self-determined time and place 
*self-directed: self-directed via online/DVD videos   
in-person: coach-facilitated via in-person groups  
videoconferencing: coach-facilitated via online groups  
 

maintenance topics, reminders to self-monitor, and contact information for the coach. The in-
person and videoconferencing approaches have a higher level of coach involvement than the 
self-directed option. Using the tracking data from participants, the lifestyle coach will provide 
individualized feedback on diet and physical activity goals during the intensive phase. 
Individualized feedback will provide ample opportunity for tailoring based on culture and other 
individual differences. During the maintenance phase, men who choose the in-person and 
videoconferencing options will receive monthly phone calls from the coach that focus on 
supporting continued goal progress  and problem solving for encountered barriers.  
 
The language options (Spanish and English) will be communicated at the orientation session so 
men can base their choice on language availability. For the self-directed option, men can view 
the videos with or without Spanish subtitles (the videos are in English). The in-person and 
video-conferencing options are also offered in either Spanish or English. Finally, family 
members, especially spouses/partners, are incorporated into the intervention. In addition to 
encouraging men to bring family members to the orientation session, the coach will recommend 
that men include family members in the program. For the self-directed option, the coach will 
encourage men to view the videos with family members. For the in-person and 
videoconferencing options, men will be encouraged to bring a family member to session 6 and 
session 12.  
 
Table 3. Level and type of coach feedback by modality 
 
Self-directed* 
In-Person* 
Videoconferencing*  
Feedback on self-monitoring logs 
     Intensive 
Per participant request 
Weekly review of diet, weight, physical activity, steps 
+ comments via MyFitnessPal 
     Maintenance 
Per participant request 
Monthly review of weight, physical activity, steps 
Email/text support 
     Intensive 
Standardized weekly 
reminders to complete 
sessions and self-
monitoring 
Standardized weekly 
reminders to attend 
sessions  
 
Standardized weekly 
reminders to attend 
sessions  
 
     Maintenance 
Standardized bi-monthly reminders for lifestyle change with handouts 
Individual support 
     Intensive 
Available as needed via phone, text, email, MyHealthOnline (MHO) 
     Maintenance 
Available as needed via 
phone, text, email, MHO 
Available as needed via 
phone, text, email, MHO, 
monthly scheduled 
individual phone consults 
Available as needed via 
phone, text, email, MHO, 
monthly scheduled 
individual phone consults 
 
*self-directed: self-directed via online videos or DVD 
in-person: coach-facilitated via in-person groups  
videoconferencing: coach-facilitated via online groups  
 
2.2.5.5 Fidelity Assurance 
We will follow recommendations for quality assurance in behavioral interventions.54  Use of 
standardized intervention materials, structured staff training and ongoing oversight are 
fundamental to ensuring high intervention fidelity. The lifestyle coach will undergo standardized 
training by a certified GLB master trainer with supplemental training on the cultural adaptations 
recommended by the Latino Patient Advisory Board. Per our standard practice, all group 
sessions will be audiotaped and a random 10% sample from the sessions by recruitment cohort 
will be audited and graded using a session-by-session rating scale from a previous trial.34  The 
coach will complete a checklist of critical intervention behaviors and materials delivered during 
each session.  Self-monitoring records and Smartphone application communication are readily 
retrievable and will be reviewed as part of routine quality control efforts.  Falling below an a 
priori performance standard (e.g., 90% adherence to intervention protocol) will trigger more 

frequent audit and feedback and, if needed, “booster” training for the coach.  Participant 
engagement and adherence are also essential to intervention fidelity and must be monitored 
and supported.  Participant progress on key intervention tracking parameters (e.g., date, format, 
duration of contact, most current weight, and physical activity level) will be routinely 
documented.  The coach will review and give feedback on homework and self-monitoring 
records and document participant progress toward protocol-specific, achievement-based 
objectives.  The coach will routinely inquire about barriers to intervention receipt and adherence, 
recommend personalized, actionable problem-solving strategies, and provide ongoing support 
via proactive follow-up. 
 
2.2.6 Participant Safety 
PCP approval will be required before potentially eligible patients are contacted by the study 
team.  Participants will be carefully screened and individuals for whom the interventions would 
be medically inappropriate or unsafe will be excluded.  Participants who develop any 
exclusionary condition (e.g., diabetes) following randomization may continue with the 
interventions and follow-up assessments with their PCP’s approval.  To ensure unbiased 
ascertainment between the intervention and control group, outcome assessors will 
systematically screen all participants for adverse events during in-person assessments at 
baseline and 18 months using a standard interview and reporting form as done in our previous 
trials.43,55-57  In addition, outcome assessors will call all participants at six and 12 months to 
screen for adverse events. Positive responses will trigger an adverse event record, which will be 
reviewed by the study clinician for seriousness, study relatedness, and expectedness.  Similar 
information reported by participants at other times (e.g., during intervention encounters) will be 
duly noted and followed, as needed, to assure participant safety.  Participants will be referred to 
their PCP for a medical evaluation as needed.  We will report adverse events to the study safety 
officer. 
 
A study safety officer will review the original protocol and any subsequent amendments, perform 
expedited monitoring of serious adverse events (SAEs) that are unexpected and related, 
perform ongoing monitoring of drop-outs, all other SAE’s, and non-SAEs, determine whether 
study procedures should be changed or the study halted due to serious safety concerns and/or 
major problems with conduct of the study, and perform periodic review of the completeness and 
validity of data to be used for analysis of safety and efficacy. The study safety officer also will 
monitor implementation of procedures to ensure research participant privacy and data 
confidentiality.  
 
2.2.7 Study Measures and Data Collection Schedule 
Assessments will occur at baseline, 6, 12, and 18 months on clinical, behavioral, and 
psychosocial outcome measures and potential effect modifiers and mediators at one of the 
PAMF clinics from which participants are recruited (Table 4).  Trained bilingual and bicultural 
research assistants blinded to participants’ random assignment will conduct assessments using 
standardized measurement protocols and equipment and previously validated interviewer-
questionnaires (in-person or by phone). For in-person data collection, participant’s preferred 
time and location will be accommodated whenever possible, including evenings and weekends. 
We will also utilize self-administered questionnaires online, which we have shown to be 
acceptable and feasible in previous trials with Latinos. We will use Spanish or English as 
indicated by the participant.  
 
2.2.7.1 Primary and Secondary Outcomes  
The primary outcome is proportion of participants who sustain at least 5% of baseline weight 
loss at 18 months. Research assistants will weigh participants in duplicate using a standard 

calibrated scale at baseline and 18 months at local clinic sites as in previous and ongoing trials. 
The primary outcome was selected based on its clinical relevance for patients, providers, and 
health care systems, as well as patient and stakeholder input. Weight loss of ≥5% of baseline 
weight is known to confer a reduction in risk of diabetes and other chronic disease.52 This 
outcome emphasizes chronic disease prevention by focusing on clinically relevant weight loss. 
Our patient advisors advocated for this primary outcome because their primary goal for the 
intervention is to reduce the risk for chronic disease. We will examine three types of secondary 
outcomes: 
 
Health behaviors (dietary intake, physical activity, and sedentary behavior) and psychosocial 
well being will be collected via phone or online questionnaire at baseline, 6, 12, and 18 months 
(Aim 2). Dietary intake will be assessed using the gold-standard approach of multiple pass 24-
hour recalls in Spanish or English by highly trained and experienced staff, over the phone, at 
baseline and 18 months.58,59 Physical activity and sedentary behavior will be assessed using the 
interviewer-administered 7-day Physical Activity Recall60,61 and a self-report measure of 
sedentary behavior62 over the phone at all time points. Participants will complete online 
questionnaires that we have successfully used in prior trials57,63 to assess psychosocial well-
being (e.g., EuroQol EQ-5D,64,65 PHQ-9 for depression66,67). If needed, a research assistant can 
help a participant fill out the online questionnaire at an office visit.  
 
Cardiometabolic risk factors (blood pressure and waist circumference) measured at baseline 
and 18 months (Aim 2). Trained research assistants will conduct blood pressure and waist 
circumference measurement according to standard protocols.68-70 Waist circumference will be 
measured in duplicate at the right iliac crest, in centimeters as per previously published 
protocols.71,72  
 
Body Weight from the EHR up to 24 months post-randomization. We will abstract weight 
measurements from the EHR 3 months prior to randomization and up to 24 months post-
randomization to examine trajectories of weight loss over time. We examined the feasibility of 
this approach by comparing data on researcher-measured weights and EHR-weights in two 
prior behavioral lifestyle intervention trials.73 Using data from two prior trials we observed that 
participants had a sufficient number of weight measurements in the EHR and there was 
excellent agreement between researcher-measured weights and weight obtained in the EHR. 
 
2.2.7.2 Potential effect modifiers and mediators 
We will collect information on potential effect modifiers at baseline including demographic 
characteristics (e.g., race/ethnicity, marital status, education), technology utilization, and health 
literacy. For technology utilization, we adapted a survey from the Pew Hispanic Trust on 
technology access and usage.74 For health literacy, we will use the newest vital sign, which 
uses a food nutrition label.75 Additionally, at each time period, we will collect information on 
theoretically-based core constructs shown to predict weight loss and behavior change in diverse 
populations including outcome expectancy, self-efficacy, and social support.47,76 These putative 
mediators are directly informed by Social Cognitive Theory, the theoretical base of HOMBRE 
and the GLB. 
 
 
 

Table 4. Outcomes, potential effect modifiers and mediators measured at baseline visit (BV), 6, 12, 
and 18 months (M) 
Outcomes 
Measures 
Mode 
BV 
6M 
12M 18M 
Primary outcome: 
Proportion losing  5% of 
baseline weight at 18 months 
Height 
In-person 
  X 
 
 
 
Weight - standardized methods70,71 
In-person 
  X 
 
   
X 
Secondary outcomes: 
Clinical: Weight change and 
cardiometabolic risk factors  
Weight 
EHR 
Ongoing -> 24 
months 
Waist circumference 
In-person 
  X 
 
 
  X 
Blood pressure- standardized 
methods69,70 
In-person 
  X 
 
 
  X 
Behavior change: 
1. Dietary intake  
2. Physical activity 
3. Sedentary behavior 
1. Multiple pass 24-hour diet 
recall58,59 
2. 7-day Physical Activity Recall;60,61 
3. Sedentary behavior 
questionnaire62 
Phone 
 
X 
X 
X 
 
 
X 
X 
 
 
X 
X 
 
X 
X 
X 
Psychosocial well-being: 
1. Health-related quality of life  
2. Obesity-specific quality of 
life 
3. Depressive symptoms 
4. Sleep habits and quality 
1. EuroQol EQ-5D64,65 
2. Obesity-related Problem Scale77,78 
3. Perceived Stress Scale & Nine-
item Patient Health Questionnaire 
(PHQ-9)66,67 
4. PROMIS™ sleep disturbance 
and Sleep-Related Impairment79 
Online 
X 
 
 
X 
Potential modifiers: 
1. Demographics 
 
2. Technology utilization 
 
3. Health literacy 
1. Age, race/ethnicity, education, 
employment, occupation, marital 
status, household size, income, 
acculturation 
In-person 
X 
 
 
 
2. Adapted from the Pew Hispanic 
Trust technology use and access 
survey74 
Online 
X 
 
 
 
3. The newest vital sign75 
In-person 
X 
 
 
 
Potential mediators: 
1. Outcome expectancy 
2. Self-efficacy 
3. Social support 
 
1. Goals and Relative Weights,80 
Stunkard silhouettes81 
2. Weight Efficacy Life-Style 
Questionnaire;82,83 Self-Efficacy 
for Dietary Change and Exercise84  
3. Social Support for Diet Change & 
Exercise85 
Online 
X 
X 
X 
X 
Intervention engagement: 
1. Session attendance 
2. Interaction with coach 
3. Self-tracking 
1. Attendance at sessions (in-clinic 
or online) and tracking of online 
video viewing 
2. Tracking of messages 
sent/received to coach 
3. Self-tracking of physical activity, 
diet, and weight on app or web 
Mobile app 
Ongoing 
 
2.2.7.3 Evaluation for implementation and dissemination 
To complement the focus on effectiveness in Aims 1 and 2, we will assess the potential for 
implementation and dissemination using the RE-AIM framework’s reach, adoption, 
implementation, and maintenance domains (Table 5),86-88 as we have done in other studies via 
interviews with study staff, coach, participant, and primary care and community 
stakeholders.57,63 We will conduct a detailed evaluation of these RE-AIM domains with mixed 
methods to gain a nuanced understanding of why the intervention is (or is not) superior to the 
minimally intensity intervention control, whether high intervention fidelity is achieved, what 
barriers and enablers there are, how these may translate into future implementation and 
dissemination, and what modifications can maximize implementation success.89 All interviews 
will be recorded and transcribed.90,91 Transcriptions of interviews with Latino patients will be 
translated to English.  

 
 
 
2.2.8 Retention  
As we have done in our previous trials,43,55-57 we will maximize adherence and retention by 
careful selection and training of staff, systematic quality control, and adhering to high-quality 
practices to maintain participation in the study.  We will use a tracking database to facilitate 
coordination and monitoring of participant-level activities.  No individuals will be randomized 
without eligibility verification or complete baseline data.  Examples of processes that facilitate 
retention at follow-up include thorough and fully informed roles and responsibilities of staff and 
participants, conveying an appreciation of participation and study identification, nominal 
remuneration for study visits, reasonable accommodations to participant schedules, and prudent 
participant incentives (pedometer and cash incentives).  We will contact participants who miss a 
visit to reschedule and re-engage them in subsequent follow-ups.  Using a combination of these 
strategies, we have consistently achieved high retention in several RCTs of similar scope (82%-
92%),34,42,94 including those with Latino participants (85%).95,96 
 
2.2.9 Statistical Analysis 
2.2.9.1 Analytical Plan 
Table 5. Summary of quantitative and qualitative measures for the process evaluation 
 
Example Questions, Data Sources, and Methods 
RE-AIM 
Domains 
Quantitative 
Qualitative 
Reach of the 
intended target 
population 
Patient survey & recruitment tracking data: 
percentage and characteristics (e.g., language 
preference, place of birth, years in the US) of 
participants compared with non-participants. 
Interviews with study staff: Barriers 
enablers of recruitment, recruitment 
variability by demographics or other 
characteristics, strategies for 
addressing barriers, potential 
strategies to maximize reach.  
Adoption by 
target staff or 
settings 
Administrative data: Characteristics of participating 
clinics, percentage and characteristics of PCPs 
approached who participated (e.g., PCPs referring 
or approving patients for study screening). 
Interviews with PCPs and clinic 
leadership: Barriers and enablers of 
clinic and provider participation, 
recommendations for reducing barriers 
and maximizing adoption. 
Implementation 
success during 
intervention 
delivery (staff 
perspective) 
Surveys of lifestyle coach: perceptions of (1) 
consistency of intervention procedures with 
available evidence, (2) intervention suitability for 
primary care, and (3) experience with the strategies 
facilitating intervention delivery based on the 
Promoting Action on Research Implementation in 
Health Services model.92,93 
Interviews with lifestyle coach, PCPs, 
and clinic leadership: Barriers and 
enablers of delivering the intervention 
and differences across participating 
clinics, modifications to maximize 
implementation success? 
Implementation 
success during 
intervention 
delivery (patient 
perspective) 
We will assess intervention participants’ 
engagement and adherence by monitoring the 
number of group sessions attended, reasons for 
missed sessions, secure messaging and self-
monitoring frequency, and adherence across 
participant subgroups. 
Interviews with a random sample of 
participants: Relevance and 
acceptability of knowledge and skills, 
frequency of practicing intervention 
strategies, perceived benefits, 
problems encountered, cultural 
congruence, satisfaction with program 
format, materials, and coach 
performance? 
Maintenance of 
intervention 
effects in 
individuals and 
settings over 
time 
We will assess attrition and adverse events by 
participant characteristics and treatment condition. 
Interviews with lifestyle coach, PCPs, 
and clinic leadership: Potential for 
integration into regular care and 
sustained, resources, policies, and 
care process redesigns needed to 
maximize sustainability. 

Aim 1 and 2: The primary hypothesis that HOMBRE will lead to a greater percentage of Latino 
men who sustain ≥5% of baseline weight loss through 18 months than the minimal intensity 
intervention control group will be tested with a generalized linear mixed model.97-99  
Y = 0 + 1 X + 2Y0 + 4+iZi + α +  + e 
 
(1) 
Let Y be the outcome of interest for a patient randomized to arm X (HOMBRE or control). Given 
the covariate-adaptive randomization, distributions of baseline values on the outcome variable 
(Y0) and key characteristics (Zi) should be similar between study arms and thus not bias the 
results. But to the extent they are associated with the outcome, their inclusion in the analysis will 
account for otherwise unexplained variation and hence increase efficiency.100  and  are clinic 
and PCP nested within clinic random effects, and e is the random error term. Aim 2 examining 
secondary outcomes at 18 can be evaluated by adapting the same model. The outcome 
variables in Aim 2 (e.g., cardiometabolic, health behavior, and psychosocial well-being 
indicators) will be continuous. We will model the change from baseline in each indicator as the 
outcome Y and the fixed and random effects will remain the same as described in model (1).     
 
Primary analyses will follow intent to treat principles. We will verify that mixed model-based 
results are not sensitive to violations of model assumptions with permutation and bootstrap 
resampling tests.101,102 We will document the extent, pattern, and reasons for missing data, and 
will conduct sensitivity analyses of the impact of missing data on stability of the primary results. 
For example, we may use weight data up to the point when they are no longer available (e.g., 
dropouts) or should not be used, and then employ multiple imputation103,104 based on a 
predictive distribution for future weights.  
 
Exploratory Aim 1: Weight loss trajectories using weight data from the EHR will be examined 
using tests of group-by-time interactions in mixed-effects growth curve model. 
Yt = 0 + 1X + 2Y0 + (3 + 4X) T + 4+iZi + α +  + b0 + b1T +  
 
(2) 
Let Yt be the outcome of interest at follow-up time T on a patient randomized to arm X 
(HOMBRE or control). Baseline values on the outcome variable (Y0) and key characteristics (Zi) 
will be included for efficiency as in model 1.100  and  are clinic and PCP nested within clinic 
random effects as in model 1, b0 and b1 account for random intercepts and random slopes, and 
 is the random error term. We will use a goodness-of-fit test to determine whether to include 
the quadratic or higher order of growth curves.  
 
Exploratory Aim 2: We will also conduct exploratory subgroup analyses to evaluate potential 
effect modifiers for the primary outcomes by expanding equation 1 to include appropriate 
modifier-by-group interaction terms. In this context, testing whether the β coefficients of the 
interaction terms are equal to zero is equivalent to testing the null hypothesis that the variable of 
interest does not independently modify the intervention effect. Mediation (e.g., change in self-
efficacy and its mediating effect on weight loss outcome) will be examined by MacKinnon’s 
product of coefficients test (αβ).106 Asymmetric confidence limits will be constructed based on 
the distribution of the product with the PRODCLIN program.107 Because multicollinearity may be 
present in multiple mediator models, we first will test each mediator in single-mediator models. 
Multiple-mediator models including all variables that are at least marginally significant in the 
single-mediator models will test for independent and suppression effects. To determine the 
extent of mediated effect, the percentage of total effect mediated will be calculated for each 
significant mediator as αβ/(αβ+ 𝛾), where 𝛾 is the direct intervention effect on outcome. The 
effect modification-mediation analyses are hypothesis-generating, but we pre-specify the 
variables to ensure a focus. 
 

Aim 3: For quantitative data collected as part of the assessment testing the potential for future 
implementation and dissemination, we will use t-tests for continuous variables and chi-square 
tests for categorical variables. For qualitative data collected related to aim 3, we will use NVivo, 
a multifunctional software system, to help code and manage data from the interview transcripts. 
We will initially use the interview guide to organize and provide a starting list of codes related to 
the factors associated with participant’s values and preferences related to weight, diet and 
physical activity which we will supplement with emergent codes to capture new factors that arise 
from the group discussion. Two project team members will independently code the transcripts. 
Discrepancies in initial coding will be solved by discussion between project team members. 
Based on these discussions, the raters will conduct a final code of the transcripts. The general 
categories of codes will relate to modality, presentation, messages or motivations for weight 
loss important to men. Intercoder agreement will be quantified and reported for each coding 
domain using Cohen’s Kappa, which is calculated as follows: (Observed agreement – Chance) / 
(1 – Chance). If substantial intercoder disagreements exist, a third project team member will 
review the text in question to determine appropriate coding before finalizing the coding 
scheme.108 
 
The purpose of the exploratory aim 2 & 3 analyses are to identify hypotheses for future rigorous 
studies. This study was not designed to have sufficient power to test the outcomes in 
exploratory analyses. In this case, multiple comparisons corrections will not be necessary.109  
   
2.2.10 Sample Size and Data interpretation 
This trial is powered on the primary outcome of percentage of participants achieving ≥5% weight 
loss at 18 months. A sample of 212 participants/arm has 80% power to detect a difference of 15 
percentage points between the HOMBRE intervention and control at =0.05 (2-sided), 
assuming at least 80% retention at 18 months based on our prior trial experiences. We 
estimated that the percentage of men randomized to the minimal intensity intervention who 
would maintain clinically significant weight loss at 18 months would be slightly lower than what 
we observed in a prior trial (39%).34 Among racial/ethnic minorities, such as Latinos, lower 
proportions have maintained clinically significant weight loss in the intervention arms (e.g., 
18%110 to 26%111). Additionally, other low intensity interventions in primary care have also 
demonstrated a lower proportion maintaining clinically significant weight loss than what was 
observed in a prior trial.9 We chose a 15-point difference in percentage of men achieving ≥5% 
weight loss at 18 months based on our prior studies and other available literature.34,112,113 
Among men randomized to the coach-led intervention in a prior trial, 51% maintained ≥5% 
weight loss at 24 months (12-point difference between groups). We anticipate a greater 
difference than observed in our prior trial, given that HOMBRE incorporates facilitated choice or 
delivery options and the control condition does not. The SHED-IT intervention tested a lower 
intensity intervention with a higher intensity intervention among Australian men over 12 months 
and found that 33% and 49% of men randomized to the respective arms maintained clinically 
significant weight loss (16 point difference).112,113  
 
2.2.11 Data Management and Quality Control  
All study data will be entered into computerized data files utilizing:  (1) Microsoft ACCESS for 
data entry on recruitment, follow-up, and intervention tracking; (2) WorldApp hosted at PAMF for 
self- and interviewer-administered questionnaire data and physical measurements; (3) the 
Nutrition Data System for Research (NDS-R) (Minneapolis, MN) licensed for data collection and 
nutrient analysis based on multiple-pass 24-hour diet recalls;58,59 and (4) a custom-designed 
web application for seven-day physical activity recall. WorldApp provides convenient multi-
lingual functionality that allows participants to easily choose their preferred language for data 
collection. All data entry systems employ automatic, real-time range, logic, and missing value 

checks.  Also, the outcome assessors are trained on data collection protocols (e.g., multiple-
pass 24-hour diet recall using NDS-R and 7-day physical activity recall), and their performance 
will be continuously monitored.  Data sets will be cleaned, verified and archived, and then 
placed into SAS (version 9.2, SAS Institute, Cary, NC) data sets, which also will be archived.  
One official copy of all study data and a master data dictionary will be maintained and updated 
regularly by the study data analyst.  All analytic and tracking databases will be stored in a 
password-protected, encrypted network drive with continuous backups.  For the protection of 
participant confidentiality, unique anonymous study IDs will be used for data storing, tracking 
and reporting.  Protected health information will be stored separately from all other study data, 
and used and disclosed in accordance with the Health Insurance Portability and Accountability 
Act regulations.  Regular reports will be produced on (1) patient accrual and follow-up 
completion/retention in relation to goals and timeline; (2) the randomization process and group 
comparability on the balancing variables; (3) key baseline characteristics of the sample, by 
(blinded) group, related to the primary and secondary outcome variables and proposed effect 
modifiers and mediators; (4) intervention exposure and adherence; and (5) protocol violations.  
Any observed delays in these processes or data irregularities will be resolved in a timely 
manner.   
 
4. Discussion 
 
Together with patients and other key stakeholders, such as healthcare providers, we designed a 
study to address the gap in evidence on how best to deliver behavioral lifestyle interventions to 
overweight and obese Latino men in primary care. The HOMBRE study is unique in that it tests 
a flexible and individualized package of delivery options for an evidence-based behavioral 
lifestyle intervention compared to a minimal intensity intervention in primary care over 18 
months.  
 
Using national data, we and others have reported that men in general and specifically Latino 
men are less likely to attempt weight loss or join weight loss programs than women.114-116 In a 
recent study, men were 60% less likely to attempt weight loss and 84% less likely to join a 
weight loss program compared to women.116 Similarly, among obese Latino adults, 42% of men 
reported attempting to lose weight compared to 61% of women.117 Diverse factors contribute to 
this disparity. Compared with women, men, including Latinos, more commonly misperceive 
themselves as normal weight when they are overweight/obese,114-117 and they experience less 
societal pressure for weight control.21,22 Rather, men’s primary motivation for weight loss 
appears to be reducing chronic disease risk factors and avoiding adverse health outcomes - the 
emphasis of the GLB program.14,21,37-39 This is particularly true for midlife and older men with 
identified risk factors39 - the target population of the HOMBRE trial.  
 
The primary care setting is ideal for capitalizing on this primary motivation for weight loss. 
However, there is limited research to inform primary care-based implementations. The 
participation of Latino men in behavioral lifestyle intervention trials based in primary care is 
difficult to quantify because ethnicity is not usually reported by sex.9 A systematic review 
documented only 546 Latino men (4%) among 15,356 participants in behavioral lifestyle 
intervention trials (not limited to primary care) where race/ethnicity was reported by sex. It is 
possible that men have not been the focus of primary care-based interventions because they 
utilize healthcare less often than women.118,119 While men may not use healthcare frequently, 
they still report a known usual place for medical care.  Studies have shown that 86% of men age 
45-64 years and 96% of men ≥65 years report a known usual place for medical care (excludes 
emergency room).120 Benefits of the primary care setting include potential for scalability given 
policy support for lifestyle interventions. The US Preventive Services Task Force recommends 

referral to behavioral lifestyle interventions for obese individuals and for overweight individuals 
with 1 or more risk factors (diabetes, pre-diabetes, hypertension, dyslipidemia, and elevated 
waist circumference) encountered in primary care. Additionally, the Center for Medicaid 
Services reimbursement policies support implementation of behavioral lifestyle interventions in 
the context of primary care. In addition to policy support, implementation in primary care offers 
the benefit of leveraging PCP support for adoption and maintenance of behavior change.  
 
The utilization of rapidly expanding health information technology modes of communication 
(e.g., Smartphone applications, Web-based applications, secure e-messaging) to provide 
culturally and linguistically appropriate weight management for high-risk Latino men in primary 
care is an important addition in this study. First, offering the option of videoconferencing is an 
innovative feature of the intervention that has not been evaluated with Latino men. We 
previously tested delivering a behavioral weight loss intervention in primary care to men only 
using Bluejeans videoconferencing (Blue Jeans™, Mountain View, CA).35 Obese men (BMI≥35 
kg/m2) randomized to the videoconferencing arm lost 3.5% (95% CI 2.1%, 4.9%) more weight 
than waitlisted controls. Second, HOMBRE incorporates technologies that enable self-
monitoring including wearable activity monitors and Smartphone applications. Despite the 
evidence that internet and mobile phone interventions have shown promise for weight loss and 
maintenance in adults,121-127 few studies of DPP translations in Latinos have incorporated 
technology. Although Latinos have historically experienced the ‘digital divide,’ their access to 
technology in general and Smartphones in particular make this a promising approach to 
maximize reach in this population.128 The HOMBRE study will fill an important gap in the 
literature by integrating health information technology with traditional care models (e.g., group 
visits) to combat obesity among Latino men.   
 
Testing a package of flexible delivery options as offered in HOMBRE presents a unique 
innovation in DPP translational research. It acknowledges a patients’ desire for individualization 
and mirrors real-world applications where patients have choice in intervention programs. Very 
few studies have examined the potential benefits of this pragmatic approach. Kramer et al.   
showed that employees (n=89) randomized to an arm that offered 2 options for receiving the 
GLB (in-person groups or individual via videos) lost significantly more weight than those 
randomized to a non-choice, weight loss control arm  (-10.4 lbs. or 5% vs. -2.3 lbs. or 1%, 
p<0.01) at 6-months.32 Piatt et al. tested 3 lifestyle intervention modalities (face-to-face groups, 
DVD, and internet education) among 555 adults and included one arm where participants could 
choose the delivery modality.33 Participants in the preference arm (n=101) lost 14.0 lbs. at 3 
months and 8.7 lbs. at 6 months, which was significantly more than other groups at 3 months 
(p<0.03), but not at 6 months.33 The HOMBRE trial will build on this limited evidence by testing a 
package of flexible delivery options compared to a minimal intensity intervention whose 
effectiveness has been previously established.34 
 
Limitations of the HOMBRE trial relate to the pragmatic study design featuring in-person study 
visits at baseline and 18 months supplemented by follow-up data from the EHR over 24 months 
and a highly flexible intervention. Using the PRECIS (Pragmatic-Explanatory Continuum 
Indicator Summary) tool as a guide, the HOMBRE trial was designed to be pragmatic and  yield 
evidence of the effectiveness of implementing an intensive lifestyle intervention in the context of 
routine primary care. As such, study design decisions favored pragmatic over explanatory 
methods. For example, while in-person follow-up visits extending to 24 months or longer is the 
gold standard for behavioral lifestyle trials, the HOMBRE trial aims to follow a large sample size 
(n=424) with lower patient burden, in part, by accessing weight data from the EHR. Other 
strategies to decrease patient burden include online self-administered questionnaires that can 
be completed at home and by phone between study visits. Additionally, the HOMBRE trial will 

provide evidence of the effectiveness of a package of flexible delivery options and is not 
designed to compare the effectiveness of delivery options offered as part of the HOMBRE arm. 
The effectiveness of each delivery option compared to a usual care control has already been 
established.34,35 These pragmatic features of the HOMBRE trial will result in robust evidence of 
the effectiveness of an intensive lifestyle intervention for a group that has been 
underrepresented in diabetes prevention translational research.  
 
Pragmatic studies, such as HOMBRE, that focus exclusively on high risk groups are critical for 
translating the benefits observed in tightly controlled efficacy trials such as the DPP into 
population-wide impacts on health disparities. Specifically, this study focuses on the priority 
population of Latino men who have been left out of diabetes prevention translational efforts to 
date,11,12,14,20 despite their high risk and significant population size. Furthering the benefit to 
address health disparities, the HOMBRE trial incorporates mixed methods to assess the 
potential for implementation and dissemination using the RE-AIM model. These data will 
supplement the comparative effectiveness trial outcomes by providing critical contextual 
information to inform implementation and dissemination in real world settings.  
 
Acknowledgments 
The project described is supported by Award Number AD 15-33394 from the Patient Centered 
Outcomes and Clinical Research Institute (PCORI) and internal funding from the Palo Alto 
Medical Foundation Research Institute.  The content is solely the responsibility of the authors 
and does not necessarily represent the official views of the PCORI or Sutter Health.  No 
sponsor or funding source has a role in the design or conduct of the study; collection, 
management, analysis or interpretation of the data; or preparation, review or approval of the 
manuscript. 
 
The authors extend special thanks to HOMBRE participants and their families who make this 
study possible.  The authors also thank the study team members who have made substantial 
contributions to the conduct of the study (Ernesto Ceja, Mayra Gonzalez, BS, Hoang Nguyen, 
BS, Veronica Luna, BS, Andrea Blonstein, MBA, RD, Elizabeth Jameiro, MD, Nancy Wittels, 
MS). 
 
 

References 
 
1. 
Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic 
burden of the projected obesity trends in the USA and the UK. Lancet. 
2011;378(9793):815-825. 
2. 
Heron M. Deaths: leading causes for 2010. National vital statistics reports : from the 
Centers for Disease Control and Prevention, National Center for Health Statistics, 
National Vital Statistics System. 2013;62(6):1-96. 
3. 
Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics--2012 
update: a report from the American Heart Association. Circulation. 2012;125(1):e2-e220. 
4. 
Ogden C, Carroll M, Fryar C, Flegal K. Prevalence of Obesity Among Adults and Youth: 
United States, 2011-2014. NCHS data brief. 2015(219):1-8. 
5. 
Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the 
distribution of body mass index among US adults, 1999-2010. JAMA : the journal of the 
American Medical Association. 2012;307(5):491-497. 
6. 
Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body 
mass index among US children and adolescents, 1999-2010. JAMA : the journal of the 
American Medical Association. 2012;307(5):483-490. 
7. 
Mozumdar A, Liguori G. Persistent increase of prevalence of metabolic syndrome among 
U.S. adults: NHANES III to NHANES 1999-2006. Diabetes Care. 2011;34(1):216-219. 
8. 
Daviglus ML, Talavera GA, Aviles-Santa ML, et al. Prevalence of major cardiovascular 
risk factors and cardiovascular diseases among Hispanic/Latino individuals of diverse 
backgrounds in the United States. JAMA : the journal of the American Medical 
Association. 2012;308(17):1775-1784. 
9. 
Wadden TA, Butryn ML, Hong PS, Tsai AG. Behavioral treatment of obesity in patients 
encountered in primary care settings: a systematic review. JAMA : the journal of the 
American Medical Association. 2014;312(17):1779-1791. 
10. 
Aguiar EJ, Morgan PJ, Collins CE, Plotnikoff RC, Callister R. Efficacy of interventions 
that include diet, aerobic and resistance training components for type 2 diabetes 
prevention: a systematic review with meta-analysis. Int J Behav Nutr Phys Act. 
2014;11:2. 
11. 
Ali MK, Echouffo-Tcheugui J, Williamson DF. How effective were lifestyle 
interventions in real-world settings that were modeled on the diabetes prevention 
program? Health Aff (Millwood). 2012;31(1):67-75. 
12. 
Whittemore R. A systematic review of the translational research on the Diabetes 
Prevention Program. Transl Behav Med. 2011;1(3):480-491. 
13. 
Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 
diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403. 
14. 
Pagoto SL, Schneider KL, Oleski JL, Luciani JM, Bodenlos JS, Whited MC. Male 
inclusion in randomized controlled trials of lifestyle weight loss interventions. Obesity. 
2012;20(6):1234-1239. 
15. 
Kramer MK, Kriska AM, Venditti EM, et al. Translating the Diabetes Prevention 
Program: a comprehensive model for prevention training and program delivery. Am J 
Prev Med. 2009;37(6):505-511. 
16. 
Kramer MK, Kriska AM, Venditti EM, et al. A novel approach to diabetes prevention: 
evaluation of the Group Lifestyle Balance program delivered via DVD. Diabetes Res 
Clin Pract. 2010;90(3):e60-63. 

17. 
Kramer MK, Miller R, Venditti E, Orchard TO. Group lifestyle intervention for diabetes 
prevention in those with metabolic syndrome in primary care practice. Diabetes Care. 
2006;55((suppl)):A517. 
18. 
McTigue KM, Conroy MB, Bigi L, Murphy C, McNeil M. Weight loss through living 
well: translating an effective lifestyle intervention into clinical practice. The Diabetes 
educator. 2009;35(2):199-204, 208. 
19. 
Kramer MK, McWilliams JR, Chen HY, Siminerio LM. A community-based diabetes 
prevention program: evaluation of the group lifestyle balance program delivered by 
diabetes educators. The Diabetes educator. 2011;37(5):659-668. 
20. 
Johnson M, Jones R, Freeman C, et al. Can diabetes prevention programmes be translated 
effectively into real-world settings and still deliver improved outcomes? A synthesis of 
evidence. Diabet Med. 2013;30(1):3-15. 
21. 
Sabinsky MS, Toft U, Raben A, Holm L. Overweight men's motivations and perceived 
barriers towards weight loss. Eur J Clin Nutr. 2007;61(4):526-531. 
22. 
Gough B. 'Real men don't diet': an analysis of contemporary newspaper representations 
of men, food and health. Soc Sci Med. 2007;64(2):326-337. 
23. 
Roux L, Ubach C, Donaldson C, Ryan M. Valuing the benefits of weight loss programs: 
an application of the discrete choice experiment. Obes Res. 2004;12(8):1342-1351. 
24. 
Muhlbacher A, Bethge S. Preferences of overweight and obese patients for weight loss 
programmes: a discrete-choice experiment. Int J Integr Care. 2013;13:e034. 
25. 
Veldwijk J, Lambooij MS, van Gils PF, Struijs JN, Smit HA, de Wit GA. Type 2 diabetes 
patients' preferences and willingness to pay for lifestyle programs: a discrete choice 
experiment. BMC Public Health. 2013;13:1099. 
26. 
Sherwood NE, Morton N, Jeffery RW, French SA, Neumark-Sztainer D, Falkner NH. 
Consumer preferences in format and type of community-based weight control programs. 
Am J Health Promot. 1998;13(1):12-18. 
27. 
Young MD, Morgan PJ, Plotnikoff RC, Callister R, Collins CE. Effectiveness of male-
only weight loss and weight loss maintenance interventions: a systematic review with 
meta-analysis. Obesity reviews : an official journal of the International Association for 
the Study of Obesity. 2012;13(5):393-408. 
28. 
Wolfe BL, Smith JE. Different strokes for different folks: why overweight men do not 
seek weight loss treatment. Eat Disord. 2002;10(2):115-124. 
29. 
Robertson LM, Douglas F, Ludbrook A, Reid G, van Teijlingen E. What works with 
men? A systematic review of health promoting interventions targeting men. BMC Health 
Serv Res. 2008;8:141. 
30. 
Morgan PJ, Warren JM, Lubans DR, Collins CE, Callister R. Engaging men in weight 
loss: Experiences of men who participated in the male only SHED-IT pilot study. Obes 
Res Clin Pract. 2011;5(3):e169-266. 
31. 
Befort C. Bridging the gender gap in behavioral obesity intervention: a comment on 
Morgan et al. Ann Behav Med. 2013;45(2):133-134. 
32. 
Kramer MK, Molenaar DM, Arena VC, et al. Improving employee health: evaluation of a 
worksite lifestyle change program to decrease risk factors for diabetes and cardiovascular 
disease. Journal of occupational and environmental medicine / American College of 
Occupational and Environmental Medicine. 2015;57(3):284-291. 

33. 
Piatt GA, Seidel MC, Powell RO, Zgibor JC. Comparative effectiveness of lifestyle 
intervention efforts in the community: results of the Rethinking Eating and ACTivity 
(REACT) study. Diabetes Care. 2013;36(2):202-209. 
34. 
Ma J, Yank V, Xiao L, et al. Translating the Diabetes Prevention Program lifestyle 
intervention for weight loss into primary care: a randomized trial. JAMA internal 
medicine. 2013;173(2):113-121. 
35. 
Azar KM, Aurora M, Wang EJ, Muzaffar A, Pressman A, Palaniappan LP. Virtual small 
groups for weight management: an innovative delivery mechanism for evidence-based 
lifestyle interventions among obese men. Transl Behav Med. 2015;5(1):37-44. 
36. 
Azar KM, Koliwad S, Poon T, et al. The Electronic CardioMetabolic Program (eCMP) 
for Patients With Cardiometabolic Risk: A Randomized Controlled Trial. J Med Internet 
Res. 2016;18(5):e134. 
37. 
Hankey CR, Leslie WS, Lean ME. Why lose weight? Reasons for seeking weight loss by 
overweight but otherwise healthy men. Int J Obes Relat Metab Disord. 2002;26(6):880-
882. 
38. 
Caperchione CM, Vandelanotte C, Kolt GS, et al. What a man wants: understanding the 
challenges and motivations to physical activity participation and healthy eating in 
middle-aged Australian men. Am J Mens Health. 2012;6(6):453-461. 
39. 
Lewis S, Thomas SL, Hyde J, Castle DJ, Komesaroff PA. A qualitative investigation of 
obese men's experiences with their weight. Am J Health Behav. 2011;35(4):458-469. 
40. 
Glasgow RE, McKay HG, Piette JD, Reynolds KD. The RE-AIM framework for 
evaluating interventions: what can it tell us about approaches to chronic illness 
management? Patient Educ Couns. 2001;44(2):119-127. 
41. 
Xiao L, Lavori PW, Wilson SR, Ma J. Comparison of dynamic block randomization and 
minimization in randomized trials: a simulation study. Clin Trials. 2011;8(1):59-69. 
42. 
Ma J, Strub P, Xiao L, et al. Behavioral weight loss and physical activity intervention in 
obese adults with asthma. A randomized trial. Annals of the American Thoracic Society. 
2015;12(1):1-11. 
43. 
Ma J, Strub P, Camargo CA, Jr., et al. The Breathe Easier through Weight Loss Lifestyle 
(BE WELL) Intervention: a randomized controlled trial. BMC Pulm Med. 2010;10:16. 
44. 
Marín G. Issues in the measurement of acculturation among Hispanics. In: Geisinger KF, 
ed. Psychological testing of Hispanics Washington, DC: American Psychological 
Association; 1992:235-251. 
45. 
Marin G, Gamba RJ. A new measurement of acculturation for Hispanics: the 
Bidimensional Acculturation Scale for Hispanics (BAS). Hisp J Behav Sci. 1996;18:297-
316. 
46. 
Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward 
an integrative model of change. J Consult Clin Psychol. 1983;51(3):390-395. 
47. 
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. 
Englewood Cliffs, N.J.: Prentice Hall; 1986. 
48. 
Diabetes Prevention Support Center. Group Lifestyle Balance Materials. In: A 
Modification of the Diabetes Prevention Program's Lifestyle Change Program. 
Pittsburgh, PA: University of Pittsburgh; 2011: 
http://www.diabetesprevention.pitt.edu/glbmaterials.aspx. Accessed May 17, 2011. 

49. 
Kramer MK, Miller RG, Siminerio LM. Evaluation of a community Diabetes Prevention 
Program delivered by diabetes educators in the United States: one-year follow up. 
Diabetes research and clinical practice. 2014;106(3):e49-52. 
50. 
Diabetes Prevention Support Center. Group Lifestyle Balance Manual of Operations. In: 
A Modification of the Diabetes Prevention Program's Lifestyle Change Program. 
Pittsburgh, PA: University of Pittsburgh; 2011: 
http://www.diabetesprevention.pitt.edu/docs/GroupLifestyleBalanceManualofOperations-
Complete.pdf. Accessed April 12, 2011. 
51. 
Centers for Disease CaP. Centers for Disease Control and Prevention Diabetes Prevention 
Recognition Program, Standards and Operating Procedures. In. Atlanta, GA2015. 
52. 
Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the 
management of overweight and obesity in adults: a report of the American College of 
Cardiology/American Heart Association Task Force on Practice Guidelines and The 
Obesity Society. Circulation. 2014;129(25 Suppl 2):S102-138. 
53. 
U.S. Department of Health and Human Services. Physical Activity Guidelines for 
Americans 2008. 2008; https://health.gov/paguidelines/pdf/paguide.pdf. Accessed June 7, 
2017. 
54. 
Bellg AJ, Borrelli B, Resnick B, et al. Enhancing treatment fidelity in health behavior 
change studies: best practices and recommendations from the NIH Behavior Change 
Consortium. Health psychology : official journal of the Division of Health Psychology, 
American Psychological Association. 2004;23(5):443-451. 
55. 
Ma J, Strub P, Lavori PW, et al. DASH for asthma: A pilot study of the DASH diet in 
not-well-controlled adult asthma. Contemporary clinical trials. 2013;35(2):55-67. 
56. 
Ma J, King AC, Wilson SR, Xiao L, Stafford RS. Evaluation of lifestyle interventions to 
treat elevated cardiometabolic risk in primary care (E-LITE): a randomized controlled 
trial. BMC Fam Pract. 2009;10:71. 
57. 
Ma J, Yank V, Lv N, et al. Research aimed at improving both mood and weight 
(RAINBOW) in primary care: A type 1 hybrid design randomized controlled trial. 
Contemporary clinical trials. 2015;43:260-278. 
58. 
Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US 
Department of Agriculture 5-step multiple-pass method in assessing food intake in obese 
and nonobese women. Am J Clin Nutr. 2003;77(5):1171-1178. 
59. 
Conway JM, Ingwersen LA, Moshfegh AJ. Accuracy of dietary recall using the USDA 
five-step multiple-pass method in men: an observational validation study. J Am Diet 
Assoc. 2004;104(4):595-603. 
60. 
Blair SN, Haskell WL, Ho P, et al. Assessment of habitual physical activity by a seven-
day recall in a community survey and controlled experiments. Am J Epidemiol. 
1985;122(5):794-804. 
61. 
Zuazagoitia A, Montoya I, Grandes G, et al. Reliability and validity of the 7-day Physical 
Activity Recall interview in a Spanish population. Eur J Sport Sci. 2012:1-8. 
62. 
Munguia-Izquierdo D, Segura-Jimenez V, Camiletti-Moiron D, et al. Spanish adaptation 
and psychometric properties of the Sedentary Behaviour Questionnaire for fibromyalgia 
patients: the al-Andalus study. Clin Exp Rheumatol. 2013;31(6 Suppl 79):S22-33. 
63. 
Rosas LG, Lv N, Xiao L, et al. Evaluation of a culturally-adapted lifestyle intervention to 
treat elevated cardiometabolic risk of Latino adults in primary care (Vida Sana): A 
randomized controlled trial. Contemporary clinical trials. 2016;48:30-40. 

64. 
EuroQoL Group Executive Office. EQ-5D User Guide. 2013; 
https://euroqol.org/publications/user-guides/. Accessed June 7, 2017. 
65. 
Sobocki P, Ekman M, Agren H, et al. Health-related quality of life measured with EQ-5D 
in patients treated for depression in primary care. Value Health. 2007;10(2):153-160. 
66. 
Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of 
PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. 
Patient Health Questionnaire. JAMA : the journal of the American Medical Association. 
1999;282(18):1737-1744. 
67. 
Diez-Quevedo C, Rangil T, Sanchez-Planell L, Kroenke K, Spitzer RL. Validation and 
utility of the patient health questionnaire in diagnosing mental disorders in 1003 general 
hospital Spanish inpatients. Psychosom Med. 2001;63(4):679-686. 
68. 
NIH and National Heart Lung Blood Institute. Clinical guidelines on the identifıcation, 
evaluation, and treatment of overweight and obesity in adults: the evidence report. 
Rockville, MD: DHHS, Public Health Service; October 1998. NIH Publication No. 00-
4084. 
69. 
Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure 
measurement in humans and experimental animals: Part 1: blood pressure measurement 
in humans: a statement for professionals from the Subcommittee of Professional and 
Public Education of the American Heart Association Council on High Blood Pressure 
Research. Hypertension. 2005;45(1):142-161. 
70. 
Measures from the PhenX Toolkit version February 4 2011, Ver 4.2 
(www.phenxtoolkit.org). PhenX (consensus measures of Phenotypes and eXposures) is 
supported by NHGRI award No. U01 HG004597. Accessed January 15, 2015. 
71. 
NHLBI. Practical Guide to the Identification, Evaluation and Treatment of Overweight 
and Obesity in Adults. Bethesda, MD: Public Health Service, U.S. Department of Health 
and Human Services; October 2000. NIH Publication No. 00-4084. 
72. 
National Health and Nutrition Examination Surveys (NHANES) Anthropometry 
Procedures Manaul. 2011; 
https://www.cdc.gov/nchs/data/nhanes/nhanes_11_12/Anthropometry_Procedures_Manu
al.pdf. Accessed June 7, 2017. 
73. 
Xiao L, Lv N, Rosas LG, Au D, Ma J. Validation of clinic weights from electronic health 
records against standardized weight measurements in weight loss trials. Obesity. 
2017;25(2):363-369. 
74. 
Pew Research Center. Technology Adoption.  
http://www.pewhispanic.org/topics/technology-adoption/. Accessed June 7, 2017. 
75. 
The New Vital Sign. 2011; 
http://www.pfizer.com/files/health/nvs_flipbook_spanish_final.pdf. Accessed June 7, 
2017. 
76. 
Palmeira AL, Teixeira PJ, Branco TL, et al. Predicting short-term weight loss using four 
leading health behavior change theories. Int J Behav Nutr Phys Act. 2007;4:14. 
77. 
Karlsson J, Taft C, Sjostrom L, Torgerson JS, Sullivan M. Psychosocial functioning in 
the obese before and after weight reduction: construct validity and responsiveness of the 
Obesity-related Problems scale. Int J Obes Relat Metab Disord. 2003;27(5):617-630. 
78. 
Bilbao A, Mar J, Mar B, Arrospide A, Martinez de Aragon G, Quintana JM. Validation 
of the Spanish translation of the questionnaire for the obesity-related problems scale. 
Obesity surgery. 2009;19(10):1393-1400. 

79. 
Yu L, Buysse DJ, Germain A, et al. Development of short forms from the PROMIS sleep 
disturbance and Sleep-Related Impairment item banks. Behavioral sleep medicine. 
2011;10(1):6-24. 
80. 
Foster GD, Wadden TA, Vogt RA, Brewer G. What is a reasonable weight loss? Patients' 
expectations and evaluations of obesity treatment outcomes. J Consult Clin Psychol. 
1997;65(1):79-85. 
81. 
Stunkard AJ, Sorensen T, Schulsinger F. Use of the Danish Adoption Register for the 
study of obesity and thinness. Res Publ Assoc Res Nerv Ment Dis. 1983;60:115-120. 
82. 
Clark MM, Abrams DB, Niaura RS, Eaton CA, Rossi JS. Self-efficacy in weight 
management. J Consult Clin Psychol. 1991;59(5):739-744. 
83. 
Ruiz VM, Berrocal C, López AE, Rivas T. Factor Analysis of the Spanish Version of the 
Weight Efficacy Life-Style Questionnaire Educational and Psychological Measurement. 
2002;62(3):539-555  
84. 
Sallis JF, Pinski RB, Grossman RM, Patterson TL, Nader PR. The development of self-
efficacy scales for health-related diet and exercise behaviors. Heath Educ Res. 
1988;3:283-292. 
85. 
Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR. The development of scales 
to measure social support for diet and exercise behaviors. Preventive medicine. 
1987;16(6):825-836. 
86. 
Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health 
promotion interventions: the RE-AIM framework. American journal of public health. 
1999;89(9):1322-1327. 
87. 
Kessler RS, Purcell EP, Glasgow RE, Klesges LM, Benkeser RM, Peek CJ. What does it 
mean to "employ" the RE-AIM model? Evaluation & the health professions. 
2013;36(1):44-66. 
88. 
Glasgow RE, Klesges LM, Dzewaltowski DA, Bull SS, Estabrooks P. The future of 
health behavior change research: what is needed to improve translation of research into 
health promotion practice? Ann Behav Med. 2004;27(1):3-12. 
89. 
Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation 
hybrid designs: combining elements of clinical effectiveness and implementation 
research to enhance public health impact. Medical care. 2012;50(3):217-226. 
90. 
Miles M, Huberman M. Qualitative Data Analysis: An Expanded Sourcebook. 2nd ed. 
Thousand Oaks, Calif: Sage; 1994. 
91. 
Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, 
procedures and measures to achieve trustworthiness. Nurse Educ Today. 2004;24(2):105-
112. 
92. 
Kitson AL, Rycroft-Malone J, Harvey G, McCormack B, Seers K, Titchen A. Evaluating 
the successful implementation of evidence into practice using the PARiHS framework: 
theoretical and practical challenges. Implementation science : IS. 2008;3:1. 
93. 
Stetler CB, Damschroder LJ, Helfrich CD, Hagedorn HJ. A Guide for applying a revised 
version of the PARIHS framework for implementation. Implementation science : IS. 
2011;6:99. 
94. 
Ma J, Strub P, Lv N, et al. Pilot randomized trial of a healthy eating behavioural 
intervention in uncontrolled asthma. Eur Respir J. 2016;47(1):122-132. 

95. 
Rosas LG, Thiyagarajan S, Goldstein BA, et al. The effectiveness of two community-
based weight loss strategies among obese, low-income US Latinos. Journal of the 
Academy of Nutrition and Dietetics. 2015;115(4):537-550 e532. 
96. 
Ma J, Berra K, Haskell WL, et al. Case management to reduce risk of cardiovascular 
disease in a county health care system. Arch Intern Med. 2009;169(21):1988-1995. 
97. 
Littell RC, Milliken GA. SAS System for Mixed Models. Cary, NC: SAS Institute Inc; 
1996. 
98. 
Brown H, Prescott R. Applied Mixed Models in Medicine. Chichester, England: John 
Wiley & Sons, Inc.; 1999. 
99. 
Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. New York: 
Springer-Verlag; 2000. 
100. 
Shao J, Yu X, Zhong B. A theory for testing hypotheses under covariate-adaptive 
randomization. Biometrika. 2010;97(2):347-360. 
101. 
Tang L, Duan N, Klap R, Asarnow JR, Belin TR. Applying permutation tests with 
adjustment for covariates and attrition weights to randomized trials of health-services 
interventions. Statistics in medicine. 2009;28(1):65-74. 
102. 
DiCiccio TJ, Efron B. Bootstrap confidence intervals. Statistic Sci. 1996;11(3):189-228. 
103. 
Robin DB. Multiple imputation for Nonresponse in Surveys. New York: John Wiley & 
Sons, Inc.; 1987. 
104. 
Lavori PW, Dawson R, Shera D. A multiple imputation strategy for clinical trials with 
truncation of patient data. Statistics in medicine. 1995;14(17):1913-1925. 
105. 
Bliese PD, Ployhart RE. Growth modeling using random coefficient models: Model 
building, testing, and illustrations. Organizational Research Methods. 2002;5(4):362-387. 
106. 
MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of 
methods to test mediation and other intervening variable effects. Psychol Methods. 
2002;7(1):83-104. 
107. 
MacKinnon DP, Fritz MS, Williams J, Lockwood CM. Distribution of the product 
confidence limits for the indirect effect: program PRODCLIN. Behav Res Methods. 
2007;39(3):384-389. 
108. 
Cohen J. A coefficient of agreement for nominal scales. Psychological Measurement. 
1960;20:27-46. 
109. 
Feise RJ. Do multiple outcome measures require p-value adjustment? BMC Med Res 
Methodol. 2002;2:8. 
110. 
Tsai AG, Wadden TA, Rogers MA, Day SC, Moore RH, Islam BJ. A primary care 
intervention for weight loss: results of a randomized controlled pilot study. Obesity. 
2010;18(8):1614-1618. 
111. 
Wadden TA, Volger S, Sarwer DB, et al. A two-year randomized trial of obesity 
treatment in primary care practice. N Engl J Med. 2011;365(21):1969-1979. 
112. 
Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. 12-month outcomes and 
process evaluation of the SHED-IT RCT: an internet-based weight loss program targeting 
men. Obesity. 2011;19(1):142-151. 
113. 
Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT randomized 
controlled trial: evaluation of an Internet-based weight-loss program for men. Obesity. 
2009;17(11):2025-2032. 

114. 
Duncan DT, Wolin KY, Scharoun-Lee M, Ding EL, Warner ET, Bennett GG. Does 
perception equal reality? Weight misperception in relation to weight-related attitudes and 
behaviors among overweight and obese US adults. Int J Behav Nutr Phys Act. 2011;8:20. 
115. 
Lemon SC, Rosal MC, Zapka J, Borg A, Andersen V. Contributions of weight 
perceptions to weight loss attempts: differences by body mass index and gender. Body 
Image. 2009;6(2):90-96. 
116. 
Tsai SA, Lv N, Xiao L, Ma J. Gender Differences in Weight-Related Attitudes and 
Behaviors Among Overweight and Obese Adults in the United States. Am J Mens Health. 
2016;10(5):389-398. 
117. 
Langellier BA, Glik D, Ortega AN, Prelip ML. Trends in racial/ethnic disparities in 
overweight self-perception among US adults, 1988-1994 and 1999-2008. Public Health 
Nutr. 2015;18(12):2115-2125. 
118. 
Pinkhasov RM, Wong J, Kashanian J, et al. Are men shortchanged on health? Perspective 
on health care utilization and health risk behavior in men and women in the United 
States. Int J Clin Pract. 2010;64(4):475-487. 
119. 
Xu F, Town M, Balluz LS, et al. Surveillance for certain health behaviors among States 
and selected local areas - United States, 2010. MMWR Surveill Summ. 2013;62(1):1-247. 
120. 
U.S. Department of Health and Human Services, Centers for Disease Control and 
Prevention, National Center for Health Statistic. Early Release of Selected Estimates 
Based on Data From the National Health Interview Survey: Usual place to go for medical 
care. 2015; https://www.cdc.gov/nchs/data/nhis/earlyrelease/earlyrelease201605_02.pdf. 
Accessed July 26, 2017. 
121. 
Neuhausera L, Kreps GL. eHealth communication and behavior change: promise and 
performance. Soc Semiot. 2010;20(1):9-27. 
122. 
Krukowski RA, West DS, Harvey-Berino J. Recent advances in internet-delivered, 
evidence-based weight control programs for adults. Journal of diabetes science and 
technology. 2009;3(1):184-189. 
123. 
Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for 
health behavior change. Preventive medicine. 2010;51(3-4):214-221. 
124. 
Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of web-based interventions in 
achieving weight loss and weight loss maintenance in overweight and obese adults: a 
systematic review with meta-analysis. Obesity reviews : an official journal of the 
International Association for the Study of Obesity. 2010;11(4):306-321. 
125. 
Arem H, Irwin M. A review of web-based weight loss interventions in adults. Obesity 
reviews : an official journal of the International Association for the Study of Obesity. 
2011;12(5):e236-243. 
126. 
Manzoni GM, Pagnini F, Corti S, Molinari E, Castelnuovo G. Internet-based behavioral 
interventions for obesity: an updated systematic review. Clinical practice and 
epidemiology in mental health : CP & EMH. 2011;7:19-28. 
127. 
Spring B, Duncan JM, Janke EA, et al. Integrating Technology Into Standard Weight 
Loss Treatment: A Randomized Controlled Trial. Arch Intern Med. 2012:1-7. 
128. 
Lopez MH, Gonzalez-Barrera A, Patten E. Closing the Digital Divide: Latinos and 
Technology Adoption. Religion. 2014.