Healthy Immigrant Community: Mobilizing the Power of Social Networks
Phase: NA
Conditions: Weight Loss
Sponsor: Mayo Clinic

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IRB Minimal Risk Protocol Template  
 
General Study Information 
 
Principal Investigator: Wieland, Mark L., M.D. and Sia, Irene G., M.D. 
 
 
 
 
 
 
 
 
Study Title: Healthy Immigrant Community 
 
Protocol version number and date: #5 – 09/28/2022 
 
Research Strategy 
1. Significance 
By many measures, immigrants arrive in US healthier than the general population.1 However, the longer they 
reside in the US, the more they approximate the risk profiles of the general population for cardiovascular disease 
(CVD) and related chronic conditions (RCC).2-8 Hispanic and African immigrants experience a racialized 
environment in the US that differs from their countries of origin.9  Repeated experiences of racism, discrimination 
and marginalization contributes to the chronic stress known to relate to worsening of health status over time and 
generations in the US.10  Approximately 15 years after immigration, the prevalence of obesity among immigrants 
to the US approximates that of US-born adults.7 In general populations, dietary behaviors and physical activity 
are associated with obesity.11-13 Among immigrant populations to high-income nations, dietary and physical 
activity behaviors are less healthy than the non-immigrant majority populations.14-17 Evidence-based health 
promotion programs in group settings are effective at changing dietary and health behaviors in general 
populations,18 but despite calls for interventions to address obesity and obesity-related behaviors among 
immigrant populations,19-21 few treatment studies have been reported.22-24  
One difficulty in designing interventions to promote weight loss and healthful behaviors is that the reasons 
for sub-optimal behaviors among immigrant groups are multiple and complex.25,26 Community-based 
participatory research (CBPR) is a means to collaboratively investigate health topics, whereby community 
members and academics partner in an equitable relationship through all phases of research and intervention 
development.27-29 This approach is well suited to intervention work that addresses the interplay between health 
Note: If this study establishes a human specimen repository (biobank) for research 
purposes, do not use this template. Use the Mayo Clinic Human Specimen Repository 
Protocol Template found on the IRB home page under Forms and Procedures at 
http://intranet.mayo.edu/charlie/irb/ 
 
First-time Use: Use this template to describe your study for a new IRB submission. 
1. Complete the questions that apply to your study. 
2. Save an electronic copy of this protocol for future revisions.    
3. When completing your IRBe application, you will be asked to upload this document to the protocol 
section. 
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protocol. Save it to your files. 
2. Open the saved document and activate “Track Changes”.  
3. Revise the protocol template to reflect the modification points, save the template to your files 
4. Create an IRBe Modification for the study and upload the revised protocol template. 

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behaviors and the social determinants of health such that it empowers communities, promotes understanding of 
culturally pertinent issues, and targets the multi-faceted barriers to health.30-33 Furthermore, CBPR is an effective 
means of approaching health topics among immigrant populations.34-41 When our partnership began in 2004, 
there were no published interventions derived in a participatory manner with immigrant communities to address 
dietary quality, physical activity, or obesity. In addition to our published work42-48, we are aware of two research 
teams pursuing similar goals through a CBPR framework, though outcomes are not yet available.49 50  
Social networks, social support, and social cohesion, particularly when grounded in activities that enhance 
cultural values and orientation, are protective against experiences of racism and marginalization that contribute 
to CVD and RCC.51 A range of social network characteristics are highly associated with obesity-related behaviors 
in general populations (nutritional intake and physical activity).52 These findings extend to obesity; a 32-year 
longitudinal study showed that a person’s chances of becoming obese increased by 57% if their friends became 
obese, which was mediated by the frequency and intensity of interactions.53 While there is debate about the 
possible confounding effects from unmeasured factors and shared environments on the results of this study,54,55 
sensitivity analyses suggest that contagion effects for obesity are robust56, and  subsequent work has 
consistently supported these findings while considering important mediating mechanisms: Structural network 
factors (e.g., size, density, etc.) may influence obesity-related behaviors through mirroring the behaviors of 
others,57,58 self-comparison59 and direct influence of peers to adopt similar behaviors.60  Functional network 
factors may shape obesity-related health behaviors through social norms,61,62 social support,63,64 and social 
cohesion,65 among other constructs.66  In total, while a causal link between social network factors and obesity 
cannot be demonstrated by a single study, a body of evidence has emerged that strongly supports the 
contribution of network constructs to the obesity epidemic in the US.66-69 
Social network interventions involve purposeful use of existing social networks in the individual’s natural 
environment to promote positive behavior change and health outcomes.70 A recent systematic review of network 
interventions reported consistent findings of positive behavior change with this approach from a variety of 
settings and across a range of health outcomes.71 Interventions incorporating social networks for diet and/or 
weight loss have been identified as a promising and innovative approach,68 but few published studies exist. 
Simulation models suggest that traditional weight loss interventions frequently fail because they lack 
consideration of the participant’s social networks, and that network-driven interventions may be highly effective.72 
Findings from a randomized trial demonstrated that spouses of partners in a weight loss program may benefit73, 
suggesting that weight loss interventions may create a contagion effect within social networks. A study 
incorporating measurement of social networks within weight loss programs have identified that network 
influences can enhance the uptake of healthful behaviors, but that careful intervention design is needed to avoid 
propagation of unhealthful obesity-related behaviors.74  
High quality experimental evidence suggests that higher homophily within social networks -the tendency of 
social contacts to be similar to one another- significantly improves the adoption of health behavior among obese 
populations.75  Among populations with social disadvantage, homophily may also widen health disparities 
through diffusion of less healthy behaviors.76-78  Homophily was found to be especially high among social 
networks of immigrants in Sweden, and immigrants in networks with a high proportion of other immigrants 
experienced poorer health than those who included a high proportion of natives in their networks.79  
The authors proposed that network closure (high homophily) may reinforce and maintain norms leading to 
negative behavior and social conditions in such networks.79  These findings are likely to be relevant to escalating 
obesity rates among immigrant populations in the US with limited English proficiency, who are less likely to use 
existing evidence-based health promotion programs than general populations.80 Therefore, health promotion 
interventions that use social networks to interface with immigrant communities may benefit from diffusion of 
positive health behaviors inherent to the programs.  
2. Innovation 
The proposed study is innovative in several ways: First, the proposed research will be the first to design and 
evaluate a social network intervention among immigrant populations for health behavior change of relevance to 

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cardiovascular risk reduction. Through the incorporation of social networks for health promotion, the study team 
will apply a highly promising intervention approach for curbing the obesity epidemic and associated 
cardiovascular risk of traditionally difficult to reach populations.  
Second, this will be achieved through a trans-disciplinary research team with expertise in CBPR, social 
network analysis, behavioral psychology, and implementation of evidence-based programs for health promotion. 
Merging CBPR with social network methodology is innovative, and we will examine the feasibility and 
acceptability of this promising public health intervention approach. 
3. Approach 
CBPR partnership experience and narrative 
This application builds on our team’s experience with CBPR through the Rochester Healthy Community 
Partnership (RHCP), a 16-year community-academic partnership.81  RHCP community and academic partners 
have collectively derived the approach. RHCP has developed an effective community-based research 
infrastructure that has facilitated extensive research training for community partners.82,83 RHCP has become 
productive and experienced at deploying data-driven programming among immigrant populations.84-87 RHCP first 
obtained extramural funding in 2008 through the NIH Partners in Research Program to strengthen the CBPR 
partnership through the development of a culturally sensitive health literacy infrastructure for immigrant 
populations. In 2011, RHCP received a grant for the Healthy Immigrant Families study described below. In 2018, 
RHCP received funding for a digital storytelling intervention to improve diabetes self-management behaviors and 
outcomes among immigrant populations.  Community and academic partners jointly conduct every phase of the 
research and disseminate research results together at community forums and academic meetings as well as co-
authorship on scientific publications and presentations.  
The study team is uniquely suited to conduct the proposed work. Drs. Wieland and Sia have expertise in 
designing and implementing health promotion interventions through a CBPR approach.44,86 Dr. Valente is an 
expert in social network interventions.88,89 Dr. Patten has expertise in clinical trials of community-based 
behavioral health interventions.90-94 Dr. Clark has expertise in behavioral weight management.95 Dr. Leppin is 
experienced in implementation and clinical integration of evidence-based health promotion programs, and 
oversees the WellConnect program.96,97 Dr. Herrin has expertise in both cluster randomized studies and in the 
use of social networks in health research.98-101 Together with community partners named on this grant (Osman, 
Goodson, Nur, Porraz Capetillo), this team has worked together closely for up to 15 years. 
 
Specific Aims 
Immigrants often arrive to Minnesota with healthier cardiovascular risk profiles than the general population, 
but these health advantages disappear over time, and rates of obesity quickly increase. Accumulation of 
cardiovascular risk is precipitated, in part, by systemic factors that promote the adoption of unhealthy behaviors 
after immigration; namely a sedentary lifestyle and consumption of calorie dense foods. Yet few strategies exist 
to effectively promote health behavior change in immigrant communities, and clinical health promotion programs 
are often difficult to access or utilize by patients with limited English proficiency.  
Rochester Healthy Community Partnership (RHCP) is a 15-year community-based participatory research 
(CBPR) partnership in Southeast MN that develops evidence-based health promotion interventions among 
immigrant groups. In our “Healthy Immigrant Families” project, RHCP partners co-created a face-to-face 
intervention of 12 modules delivered to Somali and Hispanic participants by bilingual interventionists. At 12 
months, there were significant improvements in the primary outcome of dietary quality compared with controls, 
suggesting this CBPR program could meet the unique needs of immigrant communities. However, the intensity 
of the intervention limits its dissemination potential, and the intervention did not explicitly address healthy weight 
loss nor specifically target overweight or obese participants.   
RHCP partners then conducted a survey and social network analysis among 1,301 Hispanic (n=610) and 
Somali (n=691) adults in Rochester, MN and found that obesity clustered by networks and that weight loss 
intentions were mediated  by social norms for obesity, social support, and social cohesion. This prompted the 

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development of a social network intervention, which have demonstrated efficacy across a range of health and 
psychosocial outcomes, but none have focused on immigrant communities. Therefore, RHCP partners re-
designed the Healthy Immigrant Families intervention materials specifically for overweight and obese adults from 
Hispanic and Somali communities to be delivered by peer interventionists within their social networks. The 
resultant group-based social network intervention was pilot tested with 4 groups (2 Somali, 2 Hispanic; total 
N=39), and despite the onset of coronavirus disease 2019 and a change from in-person to remote groups, the 
participants demonstrated significant reduction in weight, blood pressure, and cholesterol as well as 
improvements in their dietary quality and physical activity at 3 months (100% retention rate).  
   The objective of this work is to leverage existing social networks for health behavior change relevant to obesity 
and cardiovascular risk among immigrant populations in Southeast MN. In this project our team proposed to  
build on the prior success of WellConnect, a robust infrastructure developed by a member of our team for 
implementing multi-level evidence-based health promotion programs and integrating them within health care 
systems, tailoring these programs to the needs of immigrant communities. To achieve our objective, RHCP 
partners will test the efficacy of this social network intervention within WellConnect and evaluate its potential 
dissemination and sustainability as outlined in the following Specific Aims: 
Specific Aim #1:  Assess the efficacy of a social network-informed CBPR-derived health promotion 
intervention on obesity and other measures of cardiovascular risk in two immigrant communities. 
Hypothesis 1:  The intervention will reduce body mass index (BMI) and waist circumference among Somali 
and Hispanic participants with BMI≥25 (goal of 3% weight reduction).  Hypothesis 2:  The intervention will reduce 
cardiovascular risk profiles among Somali and Hispanic participants with BMI≥25. 
Using a stepped wedge cluster randomized design,  60 social network sub-groups of adults ( 30 Hispanic, 
30Somali) with approximately  480 participants (approximately 7 per group) will be randomly assigned to receive 
the intervention either immediately or after a delay of one year.  The intervention will consist of 12 community-
based mentoring and education sessions and group activities, followed by 12 maintenance sessions delivered 
by trained Somali and Hispanic interventionists to members of their groups in a face-to-face or virtual format. 
Outcomes measured at baseline, 6 months, 12 months, and 24 months, will be derived from the American Heart 
Association’s “Life’s Simple 7”: BMI and waist circumference (primary outcomes),  blood pressure, fasting blood 
glucose, total cholesterol, physical activity level, dietary quality, and health-related quality of life.  We will also 
test potential mediators of treatment effect consistent with the C2DREAM conceptual model. 
The expected product will be a CBPR-derived, social network-informed intervention to reduce cardiovascular 
risk among two immigrant groups. The proposed study is innovative; it will be the first to evaluate a social network 
health promotion intervention among immigrant populations. Given that over one million immigrants arrive in the 
US annually, the potential public health impact of this work is considerable. 
 
Preliminary Data 
Healthy Immigrant Families Study: RHCP partners developed and tested a nutrition and physical activity 
intervention with immigrant families through a CBPR approach in Rochester, MN.42-44,46-48,102 Interventionists from 
the participating communities (Hispanic, Somali) were trained to deliver the intervention through 12 home visits 
during the first six months and up to 12 phone calls during the next six months. Forty-four families representing 
151 individuals were randomized, and all participating families assigned to the intervention group received 100% 
of the intervention with very high fidelity.44 At 12 months, study retention was high (91% of families, 82% of 
individuals). For adults, results showed statistically significant improvements in dietary quality compared with 
controls, with non-significant trends towards improvement in physical activity.42 This study demonstrated that 
implementing a health promotion intervention with high-level contact and intensity, as well as a rigorous, larger 
scale, randomized controlled trial, is highly feasible in these immigrant communities. 
Social Network Analysis:  We conducted an analysis of the social networks to identify factors related to 
healthy behaviors and obesity among 1,301 Somali (n=691) and Hispanic (n=610) participants (50% female) in 
Rochester, MN.  Survey respondents were recruited by bilingual study team members (who were also RHCP 

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community partners) through multiple approaches, including an RHCP “recruitment to dissemination continuum” 
that is fueled by past RHCP engagement activities and dissemination events.  
Within Hispanic and Somali immigrant populations, overweight and obesity clustered by 
ascertainable social networks for obesity and obesity-related behaviors. To understand the association 
between network characteristics and obesity, the following measures were collected from each participant: body 
mass index (BMI, calculated from weight and height measurements), dietary behaviors, and physical activity 
level.103,104  Participants were also asked about the perceived weight status, dietary quality, and physical activity 
level for each named alter. 81% of Hispanic and 50% of Somali participants were overweight or obese. Among 
named alters, 32% of Hispanic and 27% of Somali were perceived to be overweight or obese (discordance with 
the respondent sample reflects an expected underreporting bias, which skews results towards the null). Despite 
this underreporting among named alters, we found that for obese respondents, the mean percentage of 
overweight and obese network members was significantly higher than for healthy weight respondents for the 
Hispanic network (33.7% vs. 28.7%, P=<0.001)103, with a non-significant trend noted for the Somali network 
(14.1% vs. 12.4%).104 This percentage was also higher for obese respondents than for overweight respondents. 
There were similar results for obesogenic behaviors, with obese and overweight respondents having a higher 
proportion of network members with unhealthy diet and low physical activity levels in both networks.103,104  These 
findings confirm that overweight, obesity, and obesogenic behaviors generally cluster according to social 
networks in these community-based samples of Hispanic and Somali participants.     
Among Somali and Hispanic participants who were overweight or obese, the number of social 
contacts trying to lose weight was associated with weight loss intentions, and this effect was mediated 
by social norms for obesity, social support, and social cohesion.  For obese/overweight participants, we 
assessed the number of social contacts trying to lose weight, social norms for weight loss, and intention to lose 
weight in the next three months.103,104  Using logistic regression modeling, we found that if most or all of 
overweight/obese participant’s social contacts were trying to lose weight (29.3% of respondents), the odds of 
stating they were likely to lose weight was four times higher compared to other participants.   In multivariate 
logistic regression analysis, this effect was mediated by social norms for obesity, social support, and social 
cohesion.103,104  These results indicate that social contacts and normative beliefs influence weight status and 
intentions for weight control, thereby highlighting the importance of targeting social influence in the treatment 
and prevention of obesity in these high risk groups. 
Healthy Immigrant Community Pilot Study:  An RHCP dissemination event was conducted with more than 
200 leaders from Hispanic and Somali communities, where preliminary data (above) were presented, and 
community members were engaged in planning for this pilot. RHCP partners adapted the existing intervention 
framework that was developed for Hispanic and Somali communities, but changed the content to tailor the 
intervention to target weight loss among overweight and obese adults; and to provide the flexibility for delivery 
by community peer interventionists, i.e., Health Promoters (HPs), within their social networks. Twelve modules 
for healthful eating and physical activity were adapted from existing evidence-based resources105-107 by working 
groups of RHCP community and academic partners. The modules were culturally tailored for the audience by 
changes to approach and content, and incorporated traditional foods in an effort to retain healthy food habits 
from their original countries while choosing nutritionally sound eating patterns from their new country.108  An 
intervention guide was derived for HPs. The intervention consisted of a 12-week behavioral weight loss 
intervention delivered by HPs (4 weeks in-person, then 8 weeks virtual).   
     The intervention was tested through a single-arm non-randomized pilot study with 4 social networks of adults 
(2 Hispanic, 2 Somali; 60.5% female) with 39 participants who were overweight or obese.  Feasibility was 
assessed by recruitment and retention rates.  Acceptability was assessed by surveys and focus groups with HPs 
and participants. Behavioral measures included servings of fruits and vegetables, drinking soda and minutes 
walking per week. Physiologic measures included weight, blood pressure, glucose, cholesterol, and triglycerides.  
Retention at post-intervention was 100% (39/39). Participants highly rated the intervention on satisfaction, 
motivation, and confidence to eat a healthy diet, be physically active, and lose weight Table 1). Participants were 

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motivated by group social support and cohesion of their long-standing social networks. On average, participants 
lost weight, lowered their blood pressure, had more servings of vegetables per day and increased their physical 
activity (Table 2). All participants completed the program despite the shift in format from in-person to virtual due 
to the COVID-19 pandemic. The decision was made by each network to continue weekly sessions, where HPs 
and network members promoted tracking, goal setting, social support, problem solving and motivational 
strategies.  New educational content was not delivered after week 4, supporting the importance of social network 
factors as a primary mechanism for behavior change in this intervention. 
 
Table 1. Participant Ratings of the Intervention (N=38) 
 
Percent 
 
 
Percent 
General reaction to the whole program 
  
After completing the program, do you feel 
confident about eating a healthy diet? 
  
  
    Really like it 
94.7 
    Think it was just OK 
5.3 
    Yes, much more confident 
91.7 
    Did not like it 
0 
    Somewhat more confident 
8.3 
Would you recommend the program to family or 
friends who are overweight? 
 
 
    No, not at all confident 
 0 
After completing the program, do you feel 
confident about being physically active? 
  
  
    Yes, definitely 
100 
    Somewhat 
0 
    Yes, much more confident 
91.9 
    No 
0 
    Somewhat more confident 
5.4 
Did participating in the program make you      want t
o do anything different to be healthy? 
 
 
    No, not at all confident 
2.7 
After completing the program, do you feel 
confident about losing weight? 
  
  
    Yes 
94 
    No 
5.3 
    Yes, much more confident 
83.3 
          Motivated to exercise 
92.1 
    Somewhat more confident 
16.7 
          Motivated to eat a healthy diet 
94.7 
    No more confident 
0 
          Motivated to lose weight 
84.2 
 
 
 
Table 2:  Biometric and health behavior measures before and after the intervention (N=39) 
 
Baseline 
Week 12 
P value 
Weight, kg (SD) 
91.6 (15.9) 
89.7 (16.6) 
< 0.0001 
Servings of fruit per day (SD) 
1.7 (1.0) 
2.9 (2.1) 
0.013 
Servings of vegetables per day (SD) 
1.9 (1.2) 
2.6 (1.4) 
0.009 
Fruits and vegetables as snacksa (SD) 
2.4 (0.9) 
3.0 (0.8) 
0.001 
Regular soda drinksb (SD) 
1.6 (0.7) 
1.3 (0.4) 
0.003 
Fruit drinks, punch, or sports drinksc (SD) 
1.7 (0.8) 
1.5 (0.6) 
0.10 
Total MET-minutes per weekd (SD) 
2689.5 (3230.8) 
6595.1 (7371.6) 
0.02 
Systolic blood pressure, mm Hg (SD) 
133.9 (16.9) 
127.2 (15.8) 
0.001 
Diastolic blood pressure, mm Hg (SD) 
81.0 (9.5) 
75.8 (9.6) 
< 0.0001 
Waist circumference, cm (SD) 
102.3 (13.7) 
100.6 (12.3) 
0.14 
Glucose, mg/dL (SD) 
90.4 (18.4) 
91.9 (11.1) 
0.73 
Total Cholesterol, mg/dL (SD) 
205.1 (49.4) 
190.8 (50.1) 
0.02 
Triglycerides, mg/dL (SD) 
206.1 (175.0) 
130.1 (78.6) 
0.001 
a4-point Likert scale of frequency in response to, “Do you eat fruits and vegetables as snacks?” 
b4-point Likert scale of frequency in response to, “Do you drink regular soda?” 
c4-point Likert scale of frequency in response to, “Do you drink fruit drinks, punch, or sport drinks?” 
dEnergy expended from carrying out physical activity as measured by the International Physical Activity Questionnaire 
 
 
Study Design and Procedures 
Aim #1: Conduct a randomized trial to assess the efficacy of a social network-informed CBPR-derived 
health promotion intervention on obesity and other measures of cardiovascular risk in two immigrant 
communities. 
Overview. The first aim address stages II-III of established methods for behavioral intervention research.110  
Using a stepped wedge cluster randomized design, 60 social network-based groups (30Hispanic, 30 Somali; 
each group with approximately 7 participants) will be randomly assigned (stratified by ethnicity, HP sex, and 
network size) to receive the intervention either immediately (step 1) or after a delay of one year (step 2). 
Outcomes, measured at baseline, 6 months, 12 months, and 24 months, will be derived from the American Heart 

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Association’s “Life’s Simple 7”: BMI and waist circumference, blood pressure, fasting blood glucose, total 
cholesterol, physical activity level, and dietary quality. We will also measure quality of life and potential mediators 
of treatment effect consistent with our theoretical framework. The intervention will consist of community-based 
mentoring and education sessions, group activities, and application of a community toolkit for healthy weight loss 
delivered by trained HPs from Hispanic and Somali communities to their social networks. During the control 
period, participants will receive printed health information developed by the study team during each assessment 
that is not directly related to diet or weight loss.    
Figure 1.  Study Design and Intervention Schedule 
 
Blinding. Study staff performing measurements and data analysis as well as all named investigators will be 
blinded to treatment condition.  
Rationale for stepped wedge design.  While randomized trials lend maximal validity to study design, the use 
of control groups in CBPR is problematic in that it may be perceived as withholding of resources that the 
community helped to create.32,111  Therefore, delayed-intervention control groups are frequently used in CBPR 
contexts.112,113 One potential concern with a delayed intervention is differential attrition in control versus 
intervention arms.  However, our prior randomized controlled trial that used the same design found high rates of 
retention in both study groups over a two-year period (above). In addition, stepped-wedge designs provide 
increased protection from bias by incorporating both historic and contemporaneous controls, as well as increased 
statistical power when there is positive within cluster correlation of outcomes over time.114 115 
Rationale for social network intervention. In the HIF study, the intervention was delivered to individual 
Hispanic and Somali families. Participants noted in post-intervention focus groups that a more flexible group-
based approach with friends and family may promote greater participation. After reviewing these data in meetings 
and an RHCP research summit, community and academic partners embraced the conceptual pivot from an 
intervention delivered among a subset of individuals to an intervention delivered through social networks. 
Community partners reported this approach as highly acceptable since social network effects are perceived as 
influential on health behaviors in Hispanic and Somali communities. On the basis of these discussions, the social 
network analysis conducted by RHCP among 1,301 participants confirmed the influence of obesogenic social 
network effects in these populations.  The subsequent pilot social network intervention was highly successful 
and enthusiastically received by participants in post-intervention focus groups. 
Rationale for addressing obesity. Minnesota Community Health Needs Assessments conducted in 2016 
demonstrated that obesity was a priority for intervention. Survey items included in the RHCP social network 
analysis of 1,301 participants indicated that dietary quality, physical activity, and obesity were among the top 
health priorities for Hispanic and Somali populations.116 These concerns mirror national trends reflecting 
increasing obesity and associated co-morbidities among immigrants after arrival to the US.7 Finally, 80% of adult 
participants in the HIF project and 66% of participants in the social network analysis were overweight or obese.117 

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Therefore, RHCP partners concluded that it is appropriate to focus on health behaviors relevant to obesity in the 
next phase of work.  
Rationale for targeting Hispanic and Somali populations. RHCP has a history of successful collaborative 
work between many different immigrant groups (and non-immigrant Latinos) towards common goals. Our recent 
RHCP partnership evaluation highlighted the positive impact of this unique collaboration on community building, 
collective advocacy, and health outcomes. For the Healthy Immigrant Families project and for the social network 
analysis, community leaders and community-based organizations from Hispanic and Somali populations have 
taken leadership roles. The work with the two groups has occurred both in parallel and in collaboration, such that 
best practices and learning opportunities have been shared throughout the process. Furthermore, these 
represent the two largest immigrant groups to Rochester, MN. If successful, our approach could be used as a 
model framework for social network level interventions in other diverse populations. 
Theoretical approach. This work aligns with the NIMHD research framework and the C2DREAM conceptual 
model by addressing interpersonal and community levels of influence in a sociocultural environment. Social 
Ecological theory postulates how structural and functional characteristics of social groups (e.g., social 
networks)118,119 influence behaviors through several pathways,68 including shared social activities and social 
support. Social networks can also indirectly influence behaviors in relation to broader conditions for health by 
addressing modifying and mediating conditions for healthy behaviors.120-123 Mediating and modifying factors 
impacting diet and physical activity among Hispanic and Somali immigrants to Southeast Minnesota were 
elucidated in our formative work, including self-efficacy and social support.43,46 Based on our past studies with  
immigrant communites,67,68 we predict that social cohesion will be an important social mediating mechanism for 
the network intervention. Higher social cohesion, i.e., close relationships among community members with strong 
mutual trust and reciprocity124, has been associated with a range of positive health outcomes.125-127 For the 
proposed intervention, the empiric relationship between cohesion as a mediator of group (e.g., social network) 
formation, maintenance, and productivity128 is especially relevant. Group cohesion is an established network-
level metric129 that has been used to assess network intervention implementation progress.130,131 Social 
networks, social support, and social cohesion, particularly when grounded in activities that enhance cultural 
values and orientation, are protective against experiences of racism and marginalization that contribute to CVD 
and RCC.51 
Figure 2. Logic Model of Intervention Strategies and Outcomes 
 
Recruitment of peer interventionists. Peer interventionists, i.e., Health Promoters (HPs), will be selected in 
two ways.  First, RHCP community partners may approach community leaders who are known to be opinion 
leaders. RHCP community partners and interventionists from the HIF study will explain the study and HP role. 
Second, HPs may be selected by RHCP community partners from opinion leaders identified in the network 
analysis. Participants were asked to name the top people they go to when they need advice, both in general and 
for specific advice about diet, physical activity, and weight loss. Both degree centrality, the measure of incoming 
and outgoing ties to a person in a network, and betweenness centrality, a measure of the extent to which a 
person lies on the shortest path in the network, were assessed as part of the identification process for the HPs. 
To maximize potential effectiveness of the intervention, the HP identification strategy was contingent upon 
certain network characteristics, namely modularity. Modularity is a measure of the structure of a social network, 
and measures the strength of the division of a network into ‘modules’ or sub-groups. A network with high 
modularity would have dense connections between people within sub-groups, but sparse connections between 

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people in different sub-groups.132,133 We calculated the modularity of the community sample to determine the 
strength of sub-group structure. We determined a priori that if modularity was high (>0.3), then HPs would be 
recruited from the leaders identified within these sub-groups, even if they were not the most nominated opinion 
leaders for the overall network. The rationale for this strategy is that in a social network with many close sub-
groups, the intervention will be most meaningful coming from a leader within a participant’s own close sub-group. 
If modularity was relatively low (<0.3), then HP’s would be recruited from those with the highest degree and 
betweenness scores in the advice networks.134,135 Our social network data demonstrated that the modularity for 
the Hispanic network was 0.94 and 0.75 for the Somali network, indicating based on our a priori criteria that HPs 
should be drawn from the most influential individuals within sub-groups rather than individuals with a combination 
of high in-degree and betweenness centrality in the overall network.  
Training of Health Promoters. We have recently described the training procedures for interventionists for the 
HIF study.136 For this project, the training was streamlined to account for delivery in networks, thereby 
enhancing intervention scalability. In the HIF intervention we learned that while startup training is important, 
ongoing training and supervision is crucial. The HPs and the intervention oversight clinicians met every two 
weeks for one hour to review the content for the next two sessions and to answer questions related to the 
ongoing groups.  We will also train health promoters on how to access and use the WellConnect portal to enter 
and track participant participation. 
 
Participants, Recruitment, and Enrollment 
Health Promoters will invite members of their social networks to participate in the study. We will limit network 
participants to a maximum of 12 per HP for purposes of measurements and the mentorship/education portion of 
the intervention. Eligibility criteria include (1) self-identification as Hispanic or Somali, (2) member of a social 
network identified in the social network analysis, (3) age≥18, (4) willingness to participate in all aspects of the 
study; and (5) provision of informed consent. Exclusion criteria include (1) pregnancy at the time of enrollment 
and (2) serious medical conditions or disabilities that would make physical activity difficult. To avoid 
stigmatization, a normal weight (BMI<25) will not exclude individuals from participating in the intervention, but 
they will be excluded from the measurements and analyses. Based on our network analysis, we expect 81% of 
Hispanic and 50% of Somali participants screened to be overweight or obese. When an HP identifies an 
individual, they will accompany a language-congruent study assistant and study coordinator to their home or 
community location, where eligibility will be determined, and informed consent and baseline measures will be 
obtained. Based on our preliminary data, we will not restrict the participants to a particular age group for either 
population, as there was no significant clustering found by age. Likewise, network data, and community 
feedback, have helped to determine that we will not restrict Hispanic groups to gender concordance, but Somali 
groups will be gender-concordant, due primarily to cultural and religious beliefs and practices. Our social network 
data also indicate that 81% of Hispanic participants and 94% of Somali participants were immigrants born outside 
the US.  The 19% of Hispanic and 6% of Somali immigrants born in the US were all first-generation immigrants 
(i.e., their parents were born outside the US).  While this study targets foreign-born immigrant populations, US-
born members of social networks will not be excluded. 
Study Participants will receive four $75 gift cards, one for each measurement time: baseline, 6 months 
measures, 12 months measures, and 24 months measures. The health promotors will receive up to $100 for 
participating in training, $600 for delivering the program during the first 6 months, and $300 for delivering the 
program in the second 6 months. 
 
Interventions  
The multi-level intervention will consist of community-based mentoring and education delivered by trained 
Hispanic and Somali HPs to their social networks. Over a period of 6 months, HPs will facilitate 12 face-to-face 
or virtual group visits.  Each of the twelve sessions will target a particular behavior for weight loss and/or reduction 
of cardiovascular risk (Table 3). All sessions will include goal setting, review of food and/or activity tracking, and 

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positive reflections. Specific strategies for weight loss include regular weigh-ins, completion of food and physical 
activity records, reducing portion sizes, planning meals, use of MyPlate for dietary proportions, removing problem 
foods from the home, increasing physical activity level, and reducing sedentary behavior. MyPlate was selected 
for this intervention since the materials are available to individuals at no cost, the information is presented in 
several languages, and participants in the HIF intervention rated MyPlate highly. 
Consistent with the theoretical framework, HPs will provide support and monitor progress on goals via six 
evidence-based strategies: Tracking, Goal Setting, Mindfulness, Social Support, Problem Solving Skills and 
Motivational Strategies.137  Participants will be informed that one of the goals is for a weight loss of 3% in 
overweight or obese individuals. Additional goals included reduction of portion sizes of calorie dense foods, a 
shift of relative dietary content to increase fruit and vegetable consumption, and 150 minutes per week of 
moderate to vigorous physical activity.  
Over the subsequent 6 months, HPs will facilitate an additional 12 virtual or face-to-face maintenance 
sessions to focus only on goal setting, review of food and/or activity tracking, and positive reflections (i.e., no 
new content will be delivered). 
Table 3.  Intervention outline and session topics 
Weekly Group Session Outline 
Content for Weekly Group Sessions 
• 
5 minutes:  Welcome 
• 
5 minutes:  Review of goals from prior week 
• 
10 minutes:  Review of food and/or activity tracking 
• 
15 minutes: New weekly topic 
• 
20 minutes:  Set SMART* goals for the following week 
• 
5 minutes: Positive reflections 
 
*Specific 
  Measureable 
  Achievable 
  Relevant 
  Timely 
1. 
My Plate; SMART goal setting; Food tracking 
2. 
My Plate: fruits and vegetables; Weigh-ins; Activity tracking 
3. 
Triggers for over-eating; Mindful eating 
4. 
Sugars in food and beverages; How to be physically active  
5. 
Fats; Healthy cooking strategies 
6. 
Importance of physical activity                     
7. 
Social support 
8. 
Stress eating; Mindful eating 
9. 
Loss of control; Managing problem foods; Social eating 
situations 
10. Planning healthy meals 
11. Self-compassion with eating 
12. How to keep going long term 
Group Session Goals 
Group Session Strategies 
• 
Reduction of portion size (My Plate) 
• 
Shift of relative dietary content to My Plate proportions 
(i.e., 50% fruits and vegetables) 
• 
150 minutes per week of moderate to vigorous physical 
activity  
• 
10,000 steps per day 
 
• 
Food records  
• 
Physical activity trackers 
• 
Weight monitoring  
• 
SMART goal setting 
• 
Feedback and re-enforcement 
• 
Modeling and social support 
• 
Problem solving 
• 
Self-efficacy enhancement 
Intervention guide. The study team, with RHCP community partners, has developed an intervention guide 
that will be printed and made available on the RHCP web site and within the program leader space on 
WellConnect. The guide consists of 3 sections: Section 1 includes the 12 content modules.  Section 2 includes 
principles of facilitating evidence-based cognitive-behavioral techniques for healthy behaviors: goal setting, 
feedback/reinforcement, self-efficacy enhancement, modeling, and problem solving.137 Section 3 includes a list 
of low or no cost community resources for physical activity and healthful eating.  
The programmatic intervention dose is lower than many health promotion interventions. The rationale is that 
1) the intervention will be more impactful when delivered by a member of participant’s social network,138 and 2) 
non-programmatic health promoting activities and interactions are likely to occur within and between social 
networks (indirect intervention effect) throughout the study interval.139 This approach highlights the potential 
advantages of social network interventions for scalability and sustainability.70,140 
The rationale for the mentorship and education component of the intervention arose from the success of this 
model in the Healthy Immigrant Families study, where the interventionists were not necessarily members of 
participant’s social networks. For this application, mentorship has the potential to carry more influence through 

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delivery within social networks by respected peers. Peer mentoring has been shown to be effective in promotion 
of healthy eating and physical activity among groups with low socioeconomic position.141,142  
 
Treatment Fidelity & Measures 
Treatment Fidelity  
In the design and conduct of this study, we have incorporated recommendations from the Treatment Fidelity 
Workgroup.143 We have recently described our process for assessing treatment fidelity for community-based 
behavior change interventions with immigrant groups in a CBPR framework.136 The HPs will utilize a manual 
(phase 1) containing an outline/script for each session and a checklist of critical topics to be covered at each 
session. Study assistants, who were interventionists for the Healthy Immigrant Families study, will randomly 
observe 5% of sessions with the HPs. These study assistants have developed expertise as health behavior 
change interventionists through 250 hours of training followed by two years of intervention delivery.136 They will 
be further trained in fidelity assessment for this study, thereby addressing the barrier of language concordance 
between interventionists and assessors. Bi-weekly meetings will be held with the HPs and study team to reinforce 
treatment fidelity, provide corrective feedback, and to conduct additional training if needed.  
Measures 
Data will be collected at 0 (baseline), 6, 12 (primary endpoint) and 24 months by trained study staff blinded 
to treatment condition. Participants will complete a survey and participate in physical measurements.  
Demographic, acculturation, and structural racism measures. The following demographic data will be 
collected at baseline: age, gender, ethnicity, country of birth, annual household income, education level, 
employment status, and use of social media. Participants will report on the following proxies for acculturation: 
years lived in the US, primary language spoken at home, and level of English language proficiency on a 5-point 
Likert scale.144 Degree of dietary acculturation145 will be assessed from results of 24-hour dietary recall described 
below. The following measures of structural racism will be obtained at baseline: Home address for automated 
collection of geolocation-based measures (Minnesota Measure of Structural Racism [MMSR], Area Deprivation 
Index [ADI], County Structural Racism Index); Everyday Racism Scale, Discrimination in Medical Settings Scale, 
and Medical Mistrust Scale.  
Primary outcome measures.  
BMI: Weight will be measured to the nearest 0.1 kg using a portable scale (Seca 880 Digital Floor Scale). 
Height will be measured to the nearest 0.1 cm at baseline only using a stadiometer. Participants will be asked to 
remove shoes prior to both measurements. BMI is calculated as weight (kg)/height squared (m2).  Waist 
circumference will be measured to the nearest 0.1 cm at the narrowest part of the torso between the ribs and 
the iliac crest. Participants will remove excess clothing prior to the measurement and smooth the remaining 
clothing against the skin. Two measurements will be taken; if the difference between the two measures is more 
than 2.0 cm, a third measurement will be taken. The average of the two closest measures will be used. 
Secondary outcome measures.  The secondary outcome measure will be the “Life's Simple 7” Composite 
Score, which is adapted from the American Heart Association standards based on health assessment data146: 
average blood pressure from 3 readings, fasting lipid panel and glucose, height, weight, self-reported cigarette 
smoking status, dietary quality, and physical activity patterns). Point values are assigned to each component: 2 
points for ideal, 1 point for intermediate, 0 points for poor. The total sum allows for a continuous measure of 
cardiovascular health ranging from poor to ideal (0-14 points). The final score will be categorized as 0-6 (poor), 
7-8 (intermediate), 9-14 (ideal).  In previous studies, adherence to the ideal Life’s Simple 7 metrics was found to 
be associated with decreased risks of cardiovascular disease147 and all-cause mortality148. 
Seated blood pressure (systolic and diastolic) measurements will be made on the right arm using an 
automated BP Tru BPM-200 after sitting quietly for five minutes.149 Blood pressure will be measured three times 
and the average of the second and third readings will be used in analyses. Fasting glucose and cholesterol will 
be collected by a single finger prick obtained by Mayo Clinic CRU staff. Participants will be asked to refrain from 
eating for at least 6 hours prior to testing.  The portable Whole Blood Lipid Screen Cholestech LDX Analyzer will 

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be used to analyze specimens, which combines enzymatic methodology and solid-phase technology measured 
by reflectance photometry to measure total cholesterol, HDL cholesterol, triglycerides and glucose in whole 
blood.  LDL cholesterol is a calculated value. Samples used for testing are whole blood from a fingerstick 
collected in a lithium heparin coated capillary tube.  Results will be recorded at the site of collection and samples 
will be destroyed upon recording of results.   
Dietary Quality: Two 24-hour dietary recalls will be collected using the Automated Self-administered 24-hour 
Recall (ASA24) system, a National Cancer Institute web-based tool that enables multiple automated self-
administered 24-hour recalls. The recalls will be performed using a computer under the supervision of study 
staff.  The tool is available in English and Spanish.  A study staff member and an interpreter will complete the 
recall with participants who speak other languages.  Through these intake estimates, the effect of the intervention 
on diet quality (Healthy Eating Index plus intake of fruits, vegetables, sweetened beverages, percent energy from 
fat and total calorie consumption) may be examined.  
Physical activity: The International Physical Activity Questionnaire (IPAQ) 156 will be used to assess the 
number of minutes of mild, moderate, and vigorous physical activity over the previous 7 days.  We have used 
objective measures of physical activity in past studies (accelerometers) as a primary outcome,44 but for this 
study the survey will be used only to observe changes over time, not as an objective measure of activity.    
Table 4. Life’s Simple 7 domains and derivation of its composite score157-159 
“Life’s Simple 7” Domain 
Optimal (2 points) 
Intermediate (1 point) 
Poor (0 points) 
Weight 
BM<25 
BMI 25-29.9 
BMI ≥30  
Blood pressure 
SBP<120 and DBP<80 
SBP 120-139 and DBP 80-89 
SBP≥140 or DBP≥90 
Total cholesterol 
<200 mg/dL 
200-239 mg/dL 
≥240 mg/dL 
Fasting glucose 
<100 mg/dL 
100-125 mg/dL 
≥126 mg/dL 
Smoking status 
Never or quit >12 months ago 
Former or quit ≤12 months ago 
Current 
Diet 
HEI≥69.3 
HEI 56.9-69.2 
HEI<56.9 
Physical activity 
≥150 min/wk moderate or ≥75 
min/wk vigorous or ≥150 min/wk 
moderate+vigorous 
1-149 min/wk moderate or 1-74 
min/wk 
vigorous 
or 
1-149 
min/wk moderate+vigorous 
None 
Composite Score 
9-14 
7-8 
0-6 
Health-related Quality of Life will be measured by single-item linear analog scale assessments of physical, 
emotional, and overall health developed by members of our research team160,161.   
Theory-based measures. Survey items will be used to assess self-efficacy and social support for diet and 
physical activity that were adapted for our HIF study from instruments developed for the Patient-centered 
Assessment and Counseling for Exercise plus Nutrition (PACE+) program for low-income, ethnically diverse 
adults162 that included nutrition self efficacy163 and social support,164 physical activity self efficacy163 and social 
support.164 Test-retest reliabilities and internal consistency reliabilities were acceptable for all of these constructs, 
and association with behaviors suggested criterion-related validity163,164.  A 6-item measure of group social 
cohesion with excellent reliability (Cronbach alpha 0.95)165 used in previous network intervention studies and in 
our pilot studies130 will be used to assess this. Number of social contacts trying to lose weight and social norms 
for weight loss will be assessed using a 4-item measure with good to excellent reliability and item loadings.61  
Validity concerns for survey instruments. While all scales described above to measure theory-based 
outcomes carry adequate validity evidence, the application of these instruments to our culturally and linguistically 
heterogenous population is problematic. In the HIF project and social network analysis (see Preliminary Data), 
we minimized this limitation through editing of the English-language version of each item with community 
partners from both groups for cultural relevance, followed by forward-translation, panel discussion, backward 
translation, a pre-test, a cognitive briefing and consensus on the final version by a core group of community 
members from each group (to reflect an accurate translation and cultural appropriateness).166 We have 
previously described this process of adapting the World Health Organization translation procedure for use with 
survey instruments in a CBPR framework.167  We used this approach for all survey measures outlined in this 
proposal, and have applied them to two studies.103,104  

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Diffusion and contamination measurement. Network interventions broadly work through social diffusion of 
behavior change through social networks.71  Diffusion can also be conceived as contamination if it occurs from 
an intervention group network into a control group network.168  Indeed, randomized controlled trials of social 
network interventions tend to underestimate intervention effects because of contamination.71,169,170  To assess 
the potential impact of contamination on the evaluation, we will measure diffusion of content to the intervention 
group and control (contamination) group through methods outlined by Simmons, et al.169  We will assess whether 
specific terminology associated with the intervention modules were recognized by network participants in both 
groups. Recognition of negative control terms (health promotion terms not associated with the intervention or 
with obesity/weight loss) will also be assessed in both groups for comparison. The difference between diffusion 
of intervention terms and negative control terms in both groups (intervention and control) will provide a measure 
of diffusion in both groups and therefore contamination in the control group.  
Measurement protocol and staff training.  Participants from individual social networks will be measured in the 
same time-frame with the HP organizing times and locations.  The measurement team will consist of one or two 
language/culture-concordant study assistants and the study coordinator.  The study assistants will be 
responsible for administrating the dietary recalls and the questionnaire. The study coordinator will be responsible 
for overseeing biometric measures. 
  
Randomization & Sample Size Considerations 
Randomization 
The units of randomization will be social network subgroups.  Randomization will occur after all HPs and their 
social network participants are enrolled and baseline measures are completed.  Randomization will be stratified 
according to ethnicity (Hispanic or Somali), age, and sex of the HPs as well as group size.   
Social network sub-groups are sufficiently discrete for randomization:  Our social network data analysis 
determined that these communities were composed of enough discrete sub-groups for randomization. An 
example of this is that the Hispanic network had 144 unique sub-groups. When considering only sub-groups 
composed of five or more individuals, there were still 88 sub-groups identified using modularity calculations. This 
is a sufficient number of sub-networks for randomization, especially when considering the approximately equal 
number of Somali networks. Therefore, the high clustering observed in our preliminary data, as demonstrated 
by the high modularity of the networks, indicates that there are clearly distinct subgroups in the networks and 
that randomization via subgroups is feasible and appropriate for the study.  
Sample size considerations 
Stepped wedge cluster randomization trials typically have more statistical power than other cluster 
randomized designs.115 This is because each cluster is able to serve as its own control, accounting directly for 
the within cluster correlation of outcomes. In addition, the impact of intra-cluster correlation on power is typically 
smaller than on conventional cluster randomized trials.171 For the current study, we expect each HP to recruit 6 
to 8 participants, so assume 7 per cluster and a substantial loss to follow up of 20% over 12 months. We assume 
a conservative intra-cluster correlation in outcomes of 0.10 to calculate the design effect DESW based on the 
formula:115,171  
where k = 2 is the number of steps (including baseline), b = 1 is the 
number of measurements per subject in the baseline step, t = 1 is the 
number of measurements per subject at each step, and n=7 is the 
number of subjects per cluster. Assuming 80% follow up and equal 
distribution across clusters, and a 80% power, we estimate the detectable standardized effects (SDs) for a range 
of numbers of enrolled HPs with a maximum probability of a Type II error of 0.05.  
       SD 
    HPs 
 N total 
0.1 
478 
1673 
0.2 
120 
420 

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For the primary endpoints, a third standard deviation (0.3SD) difference between 
groups will be considered clinically meaningful.172 On the basis of the previous 
Healthy Immigrant Families study, we will assume a 0.1 standard deviation change 
in the control group (“contamination”) so this represents a 0.4SD change in the 
intervention group. Therefore, the study will accrue 54 clusters (27 per group) with 
approximately seven participants in each cluster, for a total sample size of approximately 378 network 
participants.    
 
Data Analysis 
All analyses will be appropriate for stepped wedge trials. In the direct effects model, the co-primary endpoints 
will be changes in BMI and waist circumference from baseline to month 12 and the 12-month change in the Ajzen 
TPB, the common outcome for C2DREAM. We will use mixed effects regression models to adjust for correlations 
within social network clusters and over time.114 Models will include indicators for intervention status, step, and 
their interaction. Covariates indicated above including demographics (e.g., sex, years since arrival to the US) 
and social covariates will be included in profile analysis models to assess the sensitivity of the primary analysis 
conditional on these potential concomitant (mediating) influences. All secondary endpoints will be analyzed using 
the same methods as used for the primary endpoints.  
Missing data will be handled in several ways using an extensive library of macro procedures designed 
specifically for such tasks by the Mayo Clinic Division of Biostatistics.173 Simple imputation methods such as 
minimum, average, or maximum values carried forward will be followed by multiple imputation methods to assess 
the robustness of the study results in the presence of the missing data. All models will be assessed for goodness 
of fit, with effects reported with confidence intervals. All hypothesis tests will use a = 0.05 to determine 
significance.  
 
Bibliography 
1. 
Singh GK, Siahpush M. All-cause and cause-specific mortality of immigrants and native born in the United States. 
Am J Public Health 2001;91:392-9. 
2. 
Koya DL, Egede LE. Association between length of residence and cardiovascular disease risk factors among an 
ethnically diverse group of United States immigrants. Journal of general internal medicine 2007;22:841-6. 
3. 
Steffen PR, Smith TB, Larson M, Butler L. Acculturation to Western society as a risk factor for high blood pressure: 
a meta-analytic review. Psychosom Med 2006;68:386-97. 
4. 
Creatore MI, Moineddin R, Booth G, Manuel DH, DesMeules M, McDermott S, Glazier RH. Age- and sex-related 
prevalence of diabetes mellitus among immigrants to Ontario, Canada. CMAJ 2010;182:781-9. 
5. 
Lear SA, Humphries KH, Hage-Moussa S, Chockalingam A, Mancini GB. Immigration presents a potential increased 
risk for atherosclerosis. Atherosclerosis 2009;205:584-9. 
6. 
Lutsey PL, Diez Roux AV, Jacobs DR, Jr., Burke GL, Harman J, Shea S, Folsom AR. Associations of acculturation 
and socioeconomic status with subclinical cardiovascular disease in the multi-ethnic study of atherosclerosis. Am J Public 
Health 2008;98:1963-70. 
7. 
Goel MS, McCarthy EP, Phillips RS, Wee CC. Obesity among US immigrant subgroups by duration of residence. 
JAMA 2004;292:2860-7. 
8. 
Kaplan MS, Huguet N, Newsom JT, McFarland BH. The association between length of residence and obesity 
among Hispanic immigrants. American journal of preventive medicine 2004;27:323-6. 
9. 
Sorrell K, Khaisa S, Ecklund EH, Emerson MO. Immigrant Identities and the Shaping of a Racialized American Self. 
Socius: Sociological Research for a Dynamic World 2019;5:1-12. 
10. 
Everson-Rose SA, Lutsey PL, Roetker NS, Lewis TT, Kershaw KN, Alonso A, Diez Roux AV. Perceived 
Discrimination and Incident Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol 
2015;182:225-34. 
11. 
Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman 
A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine 
and the American Heart Association. Circulation 2007;116:1081-93. 
0.3 
53 
187 
0.4 
30 
106 
0.5 
19 
68 
0.6 
14 
48 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 15 of 22 
 
12. 
Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, Anderssen SA. Physical activity and clustered 
cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 2006;368:299-304. 
13. 
Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, Franklin B, Kris-Etherton P, Harris WS, 
Howard B, Karanja N, Lefevre M, Rudel L, Sacks F, Van Horn L, Winston M, Wylie-Rosett J. Diet and lifestyle 
recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee. 
Circulation 2006;114:82-96. 
14. 
Centers_for_Disease_Control_and_Prevention. Prevalence of fruit and vegetable consumption and physicial 
activity by race/ethnicity-United States. Morbidity and Mortality Weekly Report 2005;56:301-4. 
15. 
Crespo CJ, Smit E, Andersen RE, Carter-Pokras O, Ainsworth BE. Race/ethnicity, social class and their relation to 
physical inactivity during leisure time: results from the Third National Health and Nutrition Examination Survey, 1988-1994. 
American journal of preventive medicine 2000;18:46-53. 
16. 
Sternfeld B, Ainsworth BE, Quesenberry CP. Physical activity patterns in a diverse population of women. Preventive 
medicine 1999;28:313-23. 
17. 
Gadd M, Sundquist J, Johansson S-E, Wändell P. Do immigrants have an increased prevalence of unhealthy 
behaviours and risk factors for coronary heart disease? European Journal of Cardiovascular Prevention & Rehabilitation 
2005;12:535-41. 
18. 
Lorig K, Ritter PL, Villa FJ, Armas J. Community-based peer-led diabetes self-management: a randomized trial. 
Diabetes Educ 2009;35:641-51. 
19. 
Kandula NR, Kersey M, Lurie N. Assuring the health of immigrants: what the leading health indicators tell us. Annu 
Rev Public Health 2004;25:357-76. 
20. 
Singh GK, Kogan MD, Yu SM. Disparities in obesity and overweight prevalence among US immigrant children and 
adolescents by generational status. Journal of community health 2009;34:271-81. 
21. 
Davidson EM, Liu JJ, Bhopal RS, White M, Johnson MR, Netto G, Kakde S, Yousuf U, Sheikh A. Consideration of 
ethnicity in guidelines and systematic reviews promoting lifestyle interventions: a thematic analysis. European journal of 
public health 2014;24:508-13. 
22. 
Renzaho AM, Mellor D, Boulton K, Swinburn B. Effectiveness of prevention programmes for obesity and chronic 
diseases among immigrants to developed countries - a systematic review. Public Health Nutr;13:438-50. 
23. 
Nierkens V, Hartman MA, Nicolaou M, Vissenberg C, Beune EJ, Hosper K, van Valkengoed IG, Stronks K. 
Effectiveness of cultural adaptations of interventions aimed at smoking cessation, diet, and/or physical activity in ethnic 
minorities. a systematic review. PLoS One 2013;8:e73373. 
24. 
Bhopal RS, Douglas A, Wallia S, Forbes JF, Lean ME, Gill JM, McKnight JA, Sattar N, Sheikh A, Wild SH, 
Tuomilehto J, Sharma A, Bhopal R, Smith JB, Butcher I, Murray GD. Effect of a lifestyle intervention on weight change in 
south Asian individuals in the UK at high risk of type 2 diabetes: a family-cluster randomised controlled trial. Lancet Diabetes 
Endocrinol 2014;2:218-27. 
25. 
Dunn JR, Dyck I. Social determinants of health in Canada's immigrant population: results from the National 
Population Health Survey. Soc Sci Med 2000;51:1573-93. 
26. 
Malmusi D, Borrell C, Benach J. Migration-related health inequalities: showing the complex interactions between 
gender, social class and place of origin. Soc Sci Med 2010;71:1610-9. 
27. 
Horowitz CR, Robinson M, Seifer S. Community-based participatory research from the margin to the mainstream: 
are researchers prepared? Circulation 2009;119:2633-42. 
28. 
Shalowitz MU, Isacco A, Barquin N, Clark-Kauffman E, Delger P, Nelson D, Quinn A, Wagenaar KA. Community-
based participatory research: a review of the literature with strategies for community engagement. J Dev Behav Pediatr 
2009;30:350-61. 
29. 
Israel BA, Schulz AJ, Parker EA, Becker AB. Review of community-based research: assessing partnership 
approaches to improve public health. Annu Rev Public Health 1998;19:173-202. 
30. 
Wells K, Jones L. "Research" in community-partnered, participatory research. Jama 2009;302:320-1. 
31. 
Wallerstein NB, Duran B. Using community-based participatory research to address health disparities. Health 
promotion practice 2006;7:312-23. 
32. 
Krieger J, Allen C, Cheadle A, Ciske S, Schier JK, Senturia K, Sullivan M. Using community-based participatory 
research to address social determinants of health: lessons learned from Seattle Partners for Healthy Communities. Health 
Educ Behav 2002;29:361-82. 
33. 
Minkler M. Community-based research partnerships: challenges and opportunities. J Urban Health 2005;82:ii3-12. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 16 of 22 
 
34. 
Wieland ML, Weis, J.A., Olney, M.W., Aleman, M., Sullivan, S.M., Millington, K.L., O'Hara, C., Nigon, J.A., Sia, I.G. 
Prevalence of Latent Tuberculosis Infection at an Adult Education Center:  Results from a Community-Based Participatory 
Process. American Journal of Public Health; In Press 2011. 
35. 
Wieland ML, Weis JA, Yawn BP, Sullivan SM, Millington KL, Smith CM, Bertram S, Nigon JA, Sia IG. Perceptions 
of Tuberculosis Among Immigrants and Refugees at an Adult Education Center: A Community-Based Participatory 
Research Approach. Journal of immigrant and minority health / Center for Minority Public Health. 
36. 
Johnson CE, Ali SA, Shipp MP. Building community-based participatory research partnerships with a Somali 
refugee community. American journal of preventive medicine 2009;37:S230-6. 
37. 
Gregg J, Centurion L, Maldonado J, Aguillon R, Celaya-Alston R, Farquhar S. Interpretations of interpretations: 
combining community-based participatory research and interpretive inquiry to improve health. Prog Community Health 
Partnersh;4:149-54. 
38. 
Lam TK, McPhee SJ, Mock J, Wong C, Doan HT, Nguyen T, Lai KQ, Ha-Iaconis T, Luong TN. Encouraging 
Vietnamese-American women to obtain Pap tests through lay health worker outreach and media education. Journal of 
general internal medicine 2003;18:516-24. 
39. 
Cristancho S, Garces DM, Peters KE, Mueller BC. Listening to rural Hispanic immigrants in the Midwest: a 
community-based participatory assessment of major barriers to health care access and use. Qualitative health research 
2008;18:633-46. 
40. 
Martinez IL, Carter-Pokras O. Assessing health concerns and barriers in a heterogeneous Latino community. 
Journal of health care for the poor and underserved 2006;17:899-909. 
41. 
Lauderdale DS, Kuohung V, Chang SL, Chin MH. Identifying older Chinese immigrants at high risk for osteoporosis. 
Journal of general internal medicine 2003;18:508-15. 
42. 
Wieland ML, Hanza MMM, Weis JA, Meiers SJ, Patten CA, Clark MM, Sloan JA, Novotny PJ, Njeru JW, Abbenyi 
A, Levine JA, Goodson M, Capetillo GDP, Osman A, Hared A, Nigon JA, Sia IG. Healthy Immigrant Families: Randomized 
Controlled Trial of a Family-Based Nutrition and Physical Activity Intervention. Am J Health Promot 2018;32:473-84. 
43. 
Wieland ML, Tiedje K, Meiers SJ, Mohamed AA, Formea CM, Ridgeway JL, Asiedu GB, Boyum G, Weis JA, Nigon 
JA, Patten CA, Sia IG. Perspectives on physical activity among immigrants and refugees to a small urban community in 
Minnesota. Journal of immigrant and minority health / Center for Minority Public Health 2015;17:263-75. 
44. 
Wieland ML, Weis JA, Hanza MM, Meiers SJ, Patten CA, Clark MM, Sloan JA, Novotny PJ, Njeru JW, Abbenyi A, 
Levine JA, Goodson M, Porraz Capetillo MG, Osman A, Hared A, Nigon JA, Sia IG. Healthy immigrant families: Participatory 
development and baseline characteristics of a community-based physical activity and nutrition intervention. Contemp Clin 
Trials 2016;47:22-31. 
45. 
Wieland ML, Weis JA, Palmer T, Goodson M, Loth S, Omer F, Abbenyi A, Krucker K, Edens K, Sia IG. Physical 
activity and nutrition among immigrant and refugee women: a community-based participatory research approach. Womens 
Health Issues 2012;22:e225-32. 
46. 
Tiedje K, Wieland ML, Meiers SJ, Mohamed AA, Formea CM, Ridgeway JL, Asiedu GB, Boyum G, Weis JA, Nigon 
JA, Patten CA, Sia IG. A focus group study of healthy eating knowledge, practices, and barriers among adult and adolescent 
immigrants and refugees in the United States. The international journal of behavioral nutrition and physical activity 
2014;11:63. 
47. 
Bronars CA, Hanza MM, Meiers SJ, Patten CA, Clark MM, Nigon JA, Weis JA, Wieland ML, Sia IG. Treatment 
Fidelity Among Family Health Promoters Delivering a Physical Activity and Nutrition Intervention to Immigrant and Refugee 
Families. Health Educ Behav 2017;44:262-70. 
48. 
Hanza MM, Goodson M, Osman A, Porraz Capetillo MD, Hared A, Nigon JA, Meiers SJ, Weis JA, Wieland ML, Sia 
IG. Lessons Learned from Community-Led Recruitment of Immigrants and Refugee Participants for a Randomized, 
Community-Based Participatory Research Study. Journal of immigrant and minority health / Center for Minority Public Health 
2016;18:1241-5. 
49. 
Tovar A, Vikre EK, Gute DM, Kamins CL, Pirie A, Boulos R, Metayer N, Economos CD. Development of the live 
well curriculum for recent immigrants: a community-based participatory approach. Progress in community health 
partnerships : research, education, and action 2012;6:195-204. 
50. 
Renzaho AM, Halliday JA, Mellor D, Green J. The Healthy Migrant Families Initiative: development of a culturally 
competent obesity prevention intervention for African migrants. BMC Public Health 2015;15:272. 
51. 
Becares L, Nazroo J, Stafford M. The buffering effects of ethnic density on experienced racism and health. Health 
Place 2009;15:670-8. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 17 of 22 
 
52. 
Fletcher A, Bonell C, Sorhaindo A. You are what your friends eat: systematic review of social network analyses of 
young people's eating behaviours and bodyweight. J Epidemiol Community Health 2011;65:548-55. 
53. 
Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. The New England journal 
of medicine 2007;357:370-9. 
54. 
Cohen-Cole E, Fletcher JM. Is obesity contagious? Social networks vs. environmental factors in the obesity 
epidemic. J Health Econ 2008;27:1382-7. 
55. 
Fowler JH, Christakis NA. Estimating peer effects on health in social networks: a response to Cohen-Cole and 
Fletcher; and Trogdon, Nonnemaker, and Pais. J Health Econ 2008;27:1400-5. 
56. 
VanderWeele TJ. Sensitivity analysis for contagion effects in social networks. Sociol Methods Res 2011;40:240-55. 
57. 
Befort CA, Thomas JL, Daley CM, Rhode PC, Ahluwalia JS. Perceptions and beliefs about body size, weight, and 
weight loss among obese African American women: a qualitative inquiry. Health Educ Behav 2008;35:410-26. 
58. 
Pachucki MA, Jacques PF, Christakis NA. Social network concordance in food choice among spouses, friends, and 
siblings. Am J Public Health 2011;101:2170-7. 
59. 
Shakya HB, Christakis NA, Fowler JH. Self-comparisons as motivators for healthy behavior. Obesity 2015. 
60. 
Rancourt D, Leahey TM, LaRose JG, Crowther JH. Effects of weight-focused social comparisons on diet and activity 
outcomes in overweight and obese young women. Obesity (Silver Spring) 2015;23:85-9. 
61. 
Leahey TM, Gokee LaRose J, Fava JL, Wing RR. Social influences are associated with BMI and weight loss 
intentions in young adults. Obesity 2011;19:1157-62. 
62. 
Leahey TM, Doyle CY, Xu X, Bihuniak J, Wing RR. Social networks and social norms are associated with obesity 
treatment outcomes. Obesity (Silver Spring) 2015;23:1550-4. 
63. 
Boothe AS, Brouwer RJ, Carter-Edwards L, Ostbye T. Unmet social support for healthy behaviors among 
overweight and obese postpartum women: results from the Active Mothers Postpartum Study. J Womens Health (Larchmt) 
2011;20:1677-85. 
64. 
Mackert M, Stanforth D, Garcia AA. Undermining of nutrition and exercise decisions: experiencing negative social 
influence. Public Health Nurs 2011;28:402-10. 
65. 
Hystad P, Carpiano RM. Sense of community-belonging and health-behaviour change in Canada. J Epidemiol 
Community Health 2012;66:277-83. 
66. 
Powell K, Wilcox J, Clonan A, Bissell P, Preston L, Peacock M, Holdsworth M. The role of social networks in the 
development of overweight and obesity among adults: a scoping review. BMC Public Health 2015;15:996. 
67. 
Hammond RA. Social influence and obesity. Curr Opin Endocrinol Diabetes Obes 2010;17:467-71. 
68. 
Nam S, Redeker N, Whittemore R. Social networks and future direction for obesity research: A scoping review. 
Nurs Outlook 2015;63:299-317. 
69. 
Cunningham SA, Vaquera E, Maturo CC, Narayan KM. Is there evidence that friends influence body weight? A 
systematic review of empirical research. Soc Sci Med 2012;75:1175-83. 
70. 
Valente TW. Network interventions. Science 2012;337:49-53. 
71. 
Latkin CA, Knowlton AR. Social Network Assessments and Interventions for Health Behavior Change: A Critical 
Review. Behav Med 2015;41:90-7. 
72. 
Bahr DB, Browning RC, Wyatt HR, Hill JO. Exploiting social networks to mitigate the obesity epidemic. Obesity 
2009;17:723-8. 
73. 
Gorin AA, Wing RR, Fava JL, Jakicic JM, Jeffery R, West DS, Brelje K, Dilillo VG, Look AHERG. Weight loss 
treatment influences untreated spouses and the home environment: evidence of a ripple effect. Int J Obes (Lond) 
2008;32:1678-84. 
74. 
Shin HS, Valente TW, Riggs NR, Huh J, Spruijt-Metz D, Chou CP, Ann Pentz M. The interaction of social networks 
and child obesity prevention program effects: the pathways trial. Obesity 2014;22:1520-6. 
75. 
Centola D. An experimental study of homophily in the adoption of health behavior. Science 2011;334:1269-72. 
76. 
Pampel FC. Inequality, diffusion, and the status gradient in smoking. Social Problems 2002;49:35-57. 
77. 
Valente TW, Fujimoto K, Chou CP, Spruijt-Metz D. Adolescent affiliations and adiposity: a social network analysis 
of friendships and obesity. J Adolesc Health 2009;45:202-4. 
78. 
Rogers EM. Diffusion of Innovations. New York: Free Press; 1995. 
79. 
Rostila M. Birds of a feather flock together--and fall ill? Migrant homophily and health in Sweden. Sociol Health Illn 
2010;32:382-99. 
80. 
Hernandez-Plaza SA-M, E.; Pozo-Munoz, C. Social support interventions in migrant populations. British Journal of 
Social Work 2006;36:1151-69. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 18 of 22 
 
81. 
Rochester Healthy Community Partnership. (Accessed January 12, 2011, at www.rochesterhealthy.org.) 
82. 
Hawley NC, Wieland ML, Weis JA, Sia IG. Perceived impact of human subjects protection training on community 
partners in community-based participatory research. Progress in community health partnerships : research, education, and 
action 2014;8:241-8. 
83. 
Amico KL, Wieland ML, Weis JA, Sullivan SM, Nigon JA, Sia IG. Capacity building through focus group training in 
community-based participatory research. Education for health 2011;24:638. 
84. 
Wieland ML, Weis JA, Hanza MM, Meiers SJ, Patten CA, Clark MM, Sloan JA, Novotny PJ, Njeru JW, Abbenyi A, 
Levine JA, Goodson M, Porraz Capetillo MG, Osman A, Hared A, Nigon JA, Sia IG. Healthy immigrant families: Participatory 
development and baseline characteristics of a community-based physical activity and nutrition intervention. Contemp Clin 
Trials 2015;47:22-31. 
85. 
Wieland ML, Weis JA, Olney MW, Aleman M, Sullivan S, Millington K, O'Hara C, Nigon JA, Sia IG. Screening for 
tuberculosis at an adult education center: results of a community-based participatory process. American journal of public 
health 2011;101:1264-7. 
86. 
Njeru JW, Patten CA, Hanza MM, Brockman TA, Ridgeway JL, Weis JA, Clark MM, Goodson M, Osman A, Porraz-
Capetillo G, Hared A, Myers A, Sia IG, Wieland ML. Stories for change: development of a diabetes digital storytelling 
intervention for refugees and immigrants to minnesota using qualitative methods. BMC Public Health 2015;15:1311. 
87. 
Wieland ML, Asiedu GB, Lantz K, Abbenyi A, Njeru JW, Osman A, Goodson M, Ahmed Y, Molina LE, Doubeni CA, 
Sia IG, Rochester Healthy Community Partnership C-TF. Leveraging community engaged research partnerships for crisis 
and emergency risk communication to vulnerable populations in the COVID-19 pandemic. J Clin Transl Sci 2020;5:e6. 
88. 
Okamoto J. Scientific collaboration and team science: a social network analysis of the centers for population health 
and health disparities. Transl Behav Med 2015;5:12-23. 
89. 
Valente TW, Ritt-Olson A, Stacy A, Unger JB, Okamoto J, Sussman S. Peer acceleration: effects of a social network 
tailored substance abuse prevention program among high-risk adolescents. Addiction 2007;102:1804-15. 
90. 
Patten CA, Windsor RA, Renner CC, Enoch C, Hochreiter A, Nevak C, Smith CA, Decker PA, Bonnema S, Hughes 
CA, Brockman T. Feasibility of a tobacco cessation intervention for pregnant Alaska Native women. Nicotine Tob Res 
2010;12:79-87. 
91. 
Patten CA, Enoch C, Renner CC, Larsen K, Decker PA, Anderson KJ, Nevak C, Glasheen A, Offord KP, Lanier A. 
Evaluation of a Tobacco Educational Intervention for Pregnant Alaska Native Women. J Health Dispar Res Pract 2008;2:33-
50. 
92. 
Patten CA, Offord KP, Ames SC, Decker PA, Croghan IT, Dornelas EA, Pingree S, Boberg EW, Gustafson DH, 
Ahluwalia JS, Wolter TD, Hurt RD. Differences in adolescent smoker and nonsmoker perceptions of strategies that would 
help an adolescent quit smoking. Ann Behav Med 2003;26:124-33. 
93. 
Patten CA, Offord KP, Hurt RD, Sanderson Cox L, Thomas JL, Quigg SM, Croghan IT, Wolter TD, Decker PA. 
Training support persons to help smokers quit: a pilot study. Am J Prev Med 2004;26:386-90. 
94. 
Patten CA, Vickers KS, Martin JE, Williams CD. Exercise interventions for smokers with a history of alcoholism: 
exercise adherence rates and effect of depression on adherence. Addict Behav 2003;28:657-67. 
95. 
Clark MM, Pera V, Goldstein MG, Thebarge RW, Guise BJ. Counseling strategies for obese patients. American 
journal of preventive medicine 1996;12:266-70. 
96. 
WellConnect SE MN Partnership. (Accessed April 24, 2017, at http://www.wellconnectsemn.org/.) 
97. 
Leppin AL, Schaepe K, Egginton J, Dick S, Branda M, Christiansen L, Burow NM, Gaw C, Montori VM. Integrating 
community-based health promotion programs and primary care: a mixed methods analysis of feasibility. BMC Health Serv 
Res 2018;18:72. 
98. 
LeBlanc A, Herrin J, Williams MD, Inselman JW, Branda ME, Shah ND, Heim EM, Dick SR, Linzer M, Boehm DH, 
Dall-Winther KM, Matthews MR, Yost KJ, Shepel KK, Montori VM. Shared Decision Making for Antidepressants in Primary 
Care: A Cluster Randomized Trial. JAMA Intern Med 2015;175:1761-70. 
99. 
Finney Rutten LJ, Radecki Breitkopf C, St Sauver JL, Croghan IT, Jacobson DJ, Wilson PM, Herrin J, Jacobson 
RM. Evaluating the impact of multilevel evidence-based implementation strategies to enhance provider recommendation on 
human papillomavirus vaccination rates among an empaneled primary care patient population: a study protocol for a 
stepped-wedge cluster randomized trial. Implement Sci 2018;13:96. 
100. 
Herrin J, Soulos PR, Xu X, Gross CP, Pollack CE. An empiric approach to identifying physician peer groups from 
claims data: An example from breast cancer care. Health Serv Res 2019;54:44-51. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 19 of 22 
 
101. 
Pollack CE, Soulos PR, Herrin J, Xu X, Christakis NA, Forman HP, Yu JB, Killelea BK, Wang SY, Gross CP. The 
Impact of Social Contagion on Physician Adoption of Advanced Imaging Tests in Breast Cancer. J Natl Cancer Inst 
2017;109. 
102. 
Morrison EJ, Clark MM, Wieland ML, Weis JA, Hanza MM, Meiers SJ, Patten CA, Sloan JA, Novotny PJ, Sim LA, 
Nigon JA, Sia IG. Relationship Between Negative Mood and Health Behaviors in an Immigrant and Refugee Population. 
Journal of immigrant and minority health / Center for Minority Public Health 2016. 
103. 
Wieland ML, Njeru JW, Okamoto JM, Novotny PJ, Breen-Lyles MK, Goodson M, Porraz Capetillo GD, Molina LE, 
Sia IG. Association of social network factors with weight status and weight loss intentions among hispanic adults. J Behav 
Med 2020;43:155-65. 
104. 
Njeru JW, Wieland ML, Okamoto JM, Novotny PJ, Breen-Lyles MK, Osman A, Ahmed YA, Nur MA, Nur O, Sia IG. 
Social networks and obesity among Somali immigrants and refugees. BMC Public Health 2020;20:238. 
105. 
Gromberg J, Wells, D. Simply Good Eating for English Language Learners Minneapolis, MN: University of 
Minnesota Extension; 2007. 
106. 
Blair SN, Dunn AL, Marcus BH, Carpenter RA, Jaret P. Active Living Every Day. 2nd ed. Champaign, IL: Human 
Kinetics; 2011. 
107. 
Brownell KD. The LEARN Program for Weight Management. 10th ed. Dallas, TX: American Health Publishing 
Company; 2004. 
108. 
Ikeda JP, Pham L, Nguyen KP, Mitchell RA. Culturally relevant nutrition education improves dietary quality among 
WIC-eligible Vietnamese immigrants. J Nutr Educ Behav 2002;34:151-8. 
109. 
Green L KM. Health Promotion Planning: An Educational and Ecological Approach. 4th Edition ed. Mountain View, 
CA: Mayfield Publishers; 2005. 
110. 
Onken LS, Carroll KM, Shoham V, Cuthbert BN, Riddle M. Reenvisioning Clinical Science: Unifying the Discipline 
to Improve the Public Health. Clin Psychol Sci 2014;2:22-34. 
111. 
Buchanan DR, Miller FG, Wallerstein N. Ethical issues in community-based participatory research: balancing 
rigorous research with community participation in community intervention studies. Prog Community Health Partnersh 
2007;1:153-60. 
112. 
Ammerman A, Corbie-Smith G, St George DM, Washington C, Weathers B, Jackson-Christian B. Research 
expectations among African American church leaders in the PRAISE! project: a randomized trial guided by community-
based participatory research. Am J Public Health 2003;93:1720-7. 
113. 
Wilcox S, Laken M, Bopp M, Gethers O, Huang P, McClorin L, Parrott AW, Swinton R, Yancey A. Increasing physical 
activity among church members: community-based participatory research. American journal of preventive medicine 
2007;32:131-8. 
114. 
Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials 
2007;28:182-91. 
115. 
Woertman W, de Hoop E, Moerbeek M, Zuidema SU, Gerritsen DL, Teerenstra S. Stepped wedge designs could 
reduce the required sample size in cluster randomized trials. J Clin Epidemiol 2013;66:752-8. 
116. 
Mohamed AA, Lantz K, Ahmed YA, Osman A, Nur MA, Nur O, Njeru JW, Sia IG, Wieland ML. An Assessment of 
Health Priorities Among a Community Sample of Somali Adults. Journal of immigrant and minority health / Center for Minority 
Public Health 2021. 
117. 
Wieland ML, Weis JA, Hanza MM, Meiers SJ, Patten CA, Clark MM, Sloan JA, Novotny PJ, Njeru JW, Abbenyi A, 
Levine JA, Goodson M, Porraz Capetillo MG, Osman A, Hared A, Nigon JA, Sia IG. Healthy immigrant families: Participatory 
development and baseline characteristics of a community-based physical activity and nutrition intervention. Contemp Clin 
Trials 2015. 
118. 
Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. 
Social science & medicine 2000;51:843-57. 
119. 
Berkman LF, Glass, T. Social integration, social networks and social support and health. In: Berkman LF, Kawachi, 
I., ed. Social Epidemiology. New York: Oxford; 2000. 
120. 
Sorensen G, Barbeau E, Stoddard AM, Hunt MK, Kaphingst K, Wallace L. Promoting behavior change among 
working-class, multiethnic workers: results of the healthy directions--small business study. American journal of public health 
2005;95:1389-95. 
121. 
Sorensen G, Stoddard AM, Dubowitz T, Barbeau EM, Bigby J, Emmons KM, Berkman LF, Peterson KE. The 
influence of social context on changes in fruit and vegetable consumption: results of the healthy directions studies. American 
journal of public health 2007;97:1216-27. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 20 of 22 
 
122. 
Sorensen G, Stoddard A, Quintiliani L, Ebbeling C, Nagler E, Yang M, Pereira L, Wallace L. Tobacco use cessation 
and weight management among motor freight workers: results of the gear up for health study. Cancer Causes Control 
2010;21:2113-22. 
123. 
Emmons KM, Stoddard AM, Fletcher R, Gutheil C, Suarez EG, Lobb R, Weeks J, Bigby JA. Cancer prevention 
among working class, multiethnic adults: results of the healthy directions-health centers study. American journal of public 
health 2005;95:1200-5. 
124. 
Kawachi IS, S.V.; Daniel, K. Social Capital and Health. New York, NY: Springer; 2008. 
125. 
Inoue S, Yorifuji T, Takao S, Doi H, Kawachi I. Social cohesion and mortality: a survival analysis of older adults in 
Japan. American journal of public health 2013;103:e60-6. 
126. 
Subramanian SV, Kim DJ, Kawachi I. Social trust and self-rated health in US communities: a multilevel analysis. 
Journal of urban health : bulletin of the New York Academy of Medicine 2002;79:S21-34. 
127. 
Wen M, Cagney KA, Christakis NA. Effect of specific aspects of community social environment on the mortality of 
individuals diagnosed with serious illness. Social science & medicine 2005;61:1119-34. 
128. 
Evans CR, Dion KL. Group cohesion and performance: a meta-analysis. Small Group Research 1991;22:175-86. 
129. 
Valente TW. Social networks and health: models, methods, and applications. New York: Oxford University Press; 
2010. 
130. 
Gesell SB, Barkin SL, Valente TW. Social network diagnostics: a tool for monitoring group interventions. Implement 
Sci 2013;8:116. 
131. 
Feinberg ME, Riggs NR, Greenberg MT. Social networks and community prevention coalitions. J Prim Prev 
2005;26:279-98. 
132. 
Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A 2006;103:8577-82. 
133. 
Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci U S A 
2002;99:7821-6. 
134. 
Fortunato S, Barthelemy M. Resolution limit in community detection. Proc Natl Acad Sci U S A 2007;104:36-41. 
135. 
Arenas A, Fernandez A, Gomez S. Analysis of the structure of complex networks at different resolution levels. New 
journal of physics 2008;10:053039. 
136. 
Bronars CA, Hanza MA, Meiers SJ, Patten CA, Clark MM, Nigon JA, Weis JA, Wieland ML, Sia IG. Treatment 
Fidelity among Family Health Promoters Delivering a Physical Activity and Nutrition Intervention to Immigrant and Refugee 
Families. Health Educ Behav 2016;Accepted Manuscript In Press. 
137. 
Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, 
Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra 
K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE. Interventions to promote physical activity and 
dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart 
Association. Circulation;122:406-41. 
138. 
Kellam SG. Developing and maintaining partnerships as the foundation of implementation and implementation 
science: reflections over a half century. Adm Policy Ment Health 2012;39:317-20. 
139. 
Hunter RF, McAneney H, Davis M, Tully MA, Valente TW, Kee F. "Hidden" social networks in behavior change 
interventions. American journal of public health 2015;105:513-6. 
140. 
Valente TW, Palinkas LA, Czaja S, Chu KH, Brown CH. Social network analysis for program implementation. PLoS 
One 2015;10:e0131712. 
141. 
Perez-Escamilla R, Hromi-Fiedler A, Vega-Lopez S, Bermudez-Millan A, Segura-Perez S. Impact of peer nutrition 
education on dietary behaviors and health outcomes among Latinos: a systematic literature review. J Nutr Educ Behav 
2008;40:208-25. 
142. 
Visram S, Clarke C, White M. Making and maintaining lifestyle changes with the support of a lay health advisor: 
longitudinal qualitative study of health trainer services in northern England. PLoS One 2014;9:e94749. 
143. 
Bellg AJ, Borrelli B, Resnick B, Hecht J, Minicucci DS, Ory M, Ogedegbe G, Orwig D, Ernst D, Czajkowski S. 
Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior 
Change Consortium. Health Psychol 2004;23:443-51. 
144. 
Flores G, Abreu M, Tomany-Korman SC. Limited english proficiency, primary language at home, and disparities in 
children's health care: how language barriers are measured matters. Public Health Rep 2005;120:418-30. 
145. 
Satia-Abouta J, Patterson RE, Neuhouser ML, Elder J. Dietary acculturation: applications to nutrition research and 
dietetics. J Am Diet Assoc 2002;102:1105-18. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 21 of 22 
 
146. 
Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, 
Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, 
Yancy CW, Rosamond WD, American Heart Association Strategic Planning Task F, Statistics C. Defining and setting 
national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact 
Goal through 2020 and beyond. Circulation 2010;121:586-613. 
147. 
Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter RE, Sochor O, Coutinho T, Jensen MD, 
Roger VL, Singh P, Lopez-Jimenez F. Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality. 
Ann Intern Med 2015;163:827-35. 
148. 
Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in 
cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA 2012;307:1273-
83. 
149. 
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jr., Jones DW, Materson BJ, Oparil S, 
Wright JT, Jr., Roccella EJ. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and 
Treatment of High Blood Pressure: the JNC 7 report. Jama 2003;289:2560-72. 
156. 
Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, 
Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35:1381-
95. 
157. 
Han L, You D, Ma W, Astell-Burt T, Feng X, Duan S, Qi L. National Trends in American Heart Association Revised 
Life's Simple 7 Metrics Associated With Risk of Mortality Among US Adults. JAMA Netw Open 2019;2:e1913131. 
158. 
Mok Y, Sang Y, Ballew SH, Rebholz CM, Rosamond WD, Heiss G, Folsom AR, Coresh J, Matsushita K. American 
Heart Association's Life's Simple 7 at Middle Age and Prognosis After Myocardial Infarction in Later Life. J Am Heart Assoc 
2018;7. 
159. 
Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD, Investigators AS. Community 
prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular 
disease incidence. J Am Coll Cardiol 2011;57:1690-6. 
160. 
Locke DE, Decker PA, Sloan JA, Brown PD, Malec JF, Clark MM, Rummans TA, Ballman KV, Schaefer PL, Buckner 
JC. Validation of single-item linear analog scale assessment of quality of life in neuro-oncology patients. J Pain Symptom 
Manage 2007;34:628-38. 
161. 
Singh JA, Satele D, Pattabasavaiah S, Buckner JC, Sloan JA. Normative data and clinically significant effect sizes 
for single-item numerical linear analogue self-assessment (LASA) scales. Health Qual Life Outcomes 2014;12:187. 
162. 
Calfas KJ, Sallis JF, Oldenburg B, Ffrench M. Mediators of change in physical activity following an intervention in 
primary care: PACE. Preventive medicine 1997;26:297-304. 
163. 
Sallis JF, Pinski, R.B., Grossman, R.M., Patterson, T.L., Nader, P.R. The development of self-efficacy scales for 
health-related diet and exercise behaviors. Health Educ Behav 1988;3:283-92. 
164. 
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;16:825-36. 
165. 
Chin WW, Salisbury DW, Pearson AW, Stollak MJ. Perceived cohesion in small groups: adapting and testing the 
perceived cohesion scale in a small group setting. Small Group Research 1999;30:751-66. 
166. 
World 
Health 
Organization 
process 
of 
translation 
and 
adaptation 
of 
instruments. 
2007. 
at 
http://www.who.int/substance_abuse/research_tools/translation/.) 
167. 
Formea CM, Mohamed AA, Hassan A, Osman A, Weis JA, Sia IG, Wieland ML. Lessons learned: cultural and 
linguistic enhancement of surveys through community-based participatory research. Progress in community health 
partnerships : research, education, and action 2014;8:331-6. 
168. 
Liu L, Hudgens MG. Large sample randomization inference of causal effects in the presence of interference. J Am 
Stat Assoc 2014;109:288-301. 
169. 
Simmons N, Donnell D, Ou SS, Celentano DD, Aramrattana A, Davis-Vogel A, Metzger D, Latkin C. Assessment 
of contamination and misclassification biases in a randomized controlled trial of a social network peer education intervention 
to reduce HIV risk behaviors among drug users and risk partners in Philadelphia, PA and Chiang Mai, Thailand. AIDS Behav 
2015;19:1818-27. 
170. 
Wang K, Brown K, Shen SY, Tucker J. Social network-based interventions to promote condom use: a systematic 
review. AIDS Behav 2011;15:1298-308. 
171. 
Hemming K, Lilford R, Girling AJ. Stepped-wedge cluster randomised controlled trials: a generic framework 
including parallel and multiple-level designs. Stat Med 2015;34:181-96. 

Minimal Risk Protocol Template  
Version: #5 
 
09/28/2022 
 
 
 
 
 
 
 
 
Page 22 of 22 
 
172. 
Norman GR, Sloan JA, Wyrwich KW. Interpretation of changes in health-related quality of life: the remarkable 
universality of half a standard deviation. Med Care 2003;41:582-92. 
173. 
Rombach I, Rivero-Arias O, Gray AM, Jenkinson C, Burke O. The current practice of handling and reporting missing 
outcome data in eight widely used PROMs in RCT publications: a review of the current literature. Qual Life Res 
2016;25:1613-23. 
174. 
Green CA, Duan N, Gibbons RD, Hoagwood KE, Palinkas LA, Wisdom JP. Approaches to Mixed Methods 
Dissemination and Implementation Research: Methods, Strengths, Caveats, and Opportunities. Adm Policy Ment Health 
2015;42:508-23. 
175. 
Creswell JW, Klassen AC, Plano Clark VL, Smith KC. Best practices for mixed methods research in the health 
sciences. Bethesda (Maryland): National Institutes of Health 2011. 
176. 
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;50:217. 
177. 
Van Schaik P, Bettany-Saltikov J, Warren J. Clinical acceptance of a low-cost portable system for postural 
assessment. Behaviour & Information Technology 2002;21:47-57. 
178. 
Durlak JA, DuPre EP. Implementation matters: a review of research on the influence of implementation on program 
outcomes and the factors affecting implementation. Am J Community Psychol 2008;41:327-50. 
179. 
Harachi TW, Abbott RD, Catalano RF, Haggerty KP, Fleming CB. Opening the black box: using process evaluation 
measures to assess implementation and theory building. Am J Community Psychol 1999;27:711-31. 
180. 
Scheirer MA, Shediac MC, Cassady CE. Measuring the implementation of health promotion programs: the case of 
the Breast and Cervical Cancer Program in Maryland. Health Educ Res 1995;10:11-25. 
181. 
Oakley A, Strange V, Bonell C, Allen E, Stephenson J. Process evaluation in randomised controlled trials of complex 
interventions. Bmj 2006;332:413-6. 
182. 
Rychetnik L, Frommer M, Hawe P, Shiell A. Criteria for evaluating evidence on public health interventions. J 
Epidemiol Community Health 2002;56:119-27. 
183. 
Contandriopoulos D, Lemire M, Denis JL, Tremblay E. Knowledge exchange processes in organizations and policy 
arenas: a narrative systematic review of the literature. Milbank Q 2010;88:444-83. 
184. 
Ashcraft LE, Quinn DA, Brownson RC. Strategies for effective dissemination of research to United States 
policymakers: a systematic review. Implement Sci 2020;15:89. 
185. 
Mitton C, Adair CE, McKenzie E, Patten SB, Waye Perry B. Knowledge transfer and exchange: review and synthesis 
of the literature. Milbank Q 2007;85:729-68. 
186. 
Kelly JA, Amirkhanian YA, Kabakchieva E, Vassileva S, Vassilev B, McAuliffe TL, DiFranceisco WJ, Antonova R, 
Petrova E, Khoursine RA, Dimitrov B. Prevention of HIV and sexually transmitted diseases in high risk social networks of 
young Roma (Gypsy) men in Bulgaria: randomised controlled trial. BMJ 2006;333:1098. 
187. 
Campbell R, Starkey F, Holliday J, Audrey S, Bloor M, Parry-Langdon N, Hughes R, Moore L. An informal school-
based peer-led intervention for smoking prevention in adolescence (ASSIST): a cluster randomised trial. Lancet 
2008;371:1595-602. 
188. 
Chami GF, Ahnert SE, Kabatereine NB, Tukahebwa EM. Social network fragmentation and community health. Proc 
Natl Acad Sci U S A 2017;114:E7425-E31. 
189. 
Kim DA, Hwong AR, Stafford D, Hughes DA, O'Malley AJ, Fowler JH, Christakis NA. Social network targeting to 
maximise population behaviour change: a cluster randomised controlled trial. Lancet 2015;386:145-53. 
190. 
Leppin AL, Okamoto JM, Organick PW, Thota AD, Barrera-Flores FJ, Wieland ML, McCoy RG, Bonacci RP, Montori 
VM. Applying Social Network Analysis to Evaluate Implementation of a Multisector Population Health Collaborative That 
Uses a Bridging Hub Organization. Front Public Health 2018;6:315. 
191. 
Valente TW, Pitts SR. An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges 
and Opportunities. Annu Rev Public Health 2017;38:103-18.