Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community

Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community - NCBI Bookshelf An official website of the United States government Here's how you know The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Log in Show account info Close Account Logged in as: username Dashboard Publications Account settings Log out Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation Bookshelf Search database Books All Databases Assembly Biocollections BioProject BioSample Books ClinVar Conserved Domains dbVar Gene Genome GEO DataSets GEO Profiles GTR Identical Protein Groups MedGen MeSH NLM Catalog Nucleotide OMIM PMC Protein Protein Clusters Protein Family Models PubChem BioAssay PubChem Compound PubChem Substance PubMed SNP SRA Structure Taxonomy ToolKit ToolKitAll ToolKitBookgh Search term Search Browse Titles Advanced Help Disclaimer NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health. Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community Lisa Goldman Rosas , MPH, PhD, Jan J. Vasquez , MPH, Nan Lv , Lan Xiao , Haley Hedlin , PhD, FeiFei Qin , MPH, Adrian Kendrick , Dawn Atencio , and Randall S. Stafford , MD, PhD. Author Information and Affiliations Authors Lisa Goldman Rosas , MPH, PhD, 1 Jan J. Vasquez , MPH, 1 Nan Lv , Lan Xiao , 1 Haley Hedlin , PhD, 1 FeiFei Qin , MPH, 1 Adrian Kendrick , 2 Dawn Atencio , 2 and Randall S. Stafford , MD, PhD 1 . Affiliations 1 Stanford University, Stanford, California 2 American Indian Action Board, San Jose, California Washington (DC): Patient-Centered Outcomes Research Institute (PCORI) ; 2020 Jul . Copyright and Permissions Copyright © 2020. Stanford University School of Medicine. All Rights Reserved. This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License which permits noncommercial use and distribution provided the original author(s) and source are credited. (See https://creativecommons.org/licenses/by-nc-nd/4.0/ Structured Abstract Background: American Indian and Alaska Native (AIAN) adults are at increased risk of developing diabetes. Behavioral interventions targeting modest weight loss and moderate-to-vigorous physical activity have been shown to be effective in reducing diabetes risk. However, there has been limited success in implementing these interventions in urban AIAN communities. Objectives: There were 2 main goals: (1) to develop an enhanced version of a standard Diabetes Prevention Program (DPP) to address psychosocial stressors such as historical trauma that are posited to promote the development of diabetes and interfere with diabetes prevention; and (2) to compare the effectiveness of the enhanced intervention with the standard DPP for reducing body mass index (BMI) among AIAN adults in an urban setting. Methods: We developed a community-university partnership with local community-led governance by the American Indian Community Action Board (AICAB). The AICAB pilot tested strategies to enhance the intervention to address psychosocial stressors such as historical trauma (ie, cumulative psychological and emotional wounding across generations). Adults at risk of diabetes who reported ancestry indigenous to the Americas (Canada, United States, Mexico, Central/South America) were randomly assigned in 5 cohorts to receive either (1) a standard DPP that was culturally adapted for AIAN adults (standard intervention) or (2) the same intervention with enhancements to address psychosocial stressors (enhanced intervention). Study cohorts were formed cumulatively to ensure sufficient group size for intervention implementation in both groups. Both standard and enhanced interventions were delivered by a trained lifestyle coach over 16 weekly group sessions. Participants were followed for 12 months. The primary outcome was BMI at 12 months, calculated using height and weight, and measured by trained outcome assessors per standard protocols. Secondary outcomes were quality of life, depression, dietary intake, physical activity, and empowerment. Intention-to-treat analyses of between-treatment differences in primary and secondary outcomes tested for treatment-by-time interactions in repeated-measures mixed-effects linear models. Results: The AICAB pilot tested 3 strategies as enhancements to the DPP to address psychosocial stressors resulting from historical trauma: (1) Talking Circles, (2) modified Photovoice, and (3) digital storytelling. These strategies were added to the standard, culturally adapted DPP to create the enhanced version of the intervention. Participants (N = 205) were middle-aged (mean [SD], 52.0 [13.3] years), mostly female (78.7%), and obese (BMI, 37.5 [6.6] for men and 37.2 [6.0] for women). The enhanced intervention (n = 104) was not more effective than the standard intervention (n = 101) at 12 months for reducing BMI (mean difference in change from baseline, 0.3; 95% CI, −0.4 to 0.9; P = .39). There were also no differences in secondary outcomes with the exception of dietary intake. Participants in the standard intervention had decreased their consumption of unhealthy food at 12 months more than those in the enhanced group ( P < .05). Compared with baseline, participants randomly assigned to the enhanced group had a significantly lower BMI at 6 but not at 12 months, and participants randomly assigned to the standard group had significantly lower BMI at 6 and 12 months. Overall, 18% of participants in the enhanced group and 24% in the standard group lost at least 5% of their baseline weight. The mean (SD) number of sessions attended was 9.5 (5.7) out of 16 for the enhanced intervention group and 9.0 (5.3) for the standard intervention group. Participants who attended more group sessions in both groups were more likely to lose more weight. Conclusions: The community partner took a leadership role in planning and implementing a randomized controlled trial comparing 2 approaches to diabetes prevention among AIAN adults in an urban setting. The trial results do not support enhancing the DPP with the strategies employed in this study to address psychosocial stressors. Using the standard, culturally tailored DPP curriculum can be effective and save resources. Strategies to promote increased attendance in the intervention may be needed to increase effectiveness. Study Limitations: The study included local participants primarily with indigenous ancestry from the United States and Mexico; 14% to 16% of participants were lost to follow-up; and it is unknown if loss to follow-up was causally related to the primary outcome (ie, weight gain). Background About a third (36%) of US adults are considered obese 1 ; obesity is associated with leading causes of preventable death such as heart disease, stroke, and type 2 diabetes. 2 It is estimated that 25.8 million (14%) of US adults have diabetes, which carries a higher risk for heart disease, stroke, kidney failure, nontraumatic lower-limb amputations, and blindness. 3 , 4 An additional 79 million (38%) of adults have prediabetes, indicated by blood glucose or hemoglobin A 1c levels higher than normal but not high enough to indicate diabetes. 3 , 4 Racial/ethnic minorities and populations of low socioeconomic status (SES), such as American Indians and Alaska Natives (AIANs), are disproportionately represented among Americans classified as obese and those diagnosed with diabetes. 5-9 According to 2016 US Census Bureau estimates, 6.7 million people identify as AIAN in the United States. 10 They have a lower life expectancy and bear a disproportionate burden of disease, including high prevalence of obesity and diabetes. 3 , 11-19 Self-reported 2010 National Health Interview Survey data indicate obesity prevalence to be 38% and diabetes prevalence to be 18% in AIANs compared with 26% and 8%, respectively, in non-Hispanic Whites. 20 In addition to a high prevalence of obesity and diabetes, clinically reported mental health conditions are higher in AIANs than in other racial/ethnic groups. In particular, compared with non-Hispanic Whites, AIANs report higher rates of depression symptoms (eg, sadness some of the time, 14% vs 8%); any illicit drug use (18% vs 9%); 20 binge drinking more than once in the last month (32% vs 17%); reporting they are “not satisfied with life” (10% vs 5%); ≥14 days/month with poor mental health (18% vs 11%); 21 and serious psychological distress (5% vs 3%). Suicide rates are higher in AIAN youth and young adults but similar to those of non-Hispanic Whites at older ages. 22 Fortunately, diabetes can be prevented through proven behavioral lifestyle intervention. The landmark 2002 Diabetes Prevention Program (DPP) clinical trial demonstrated that a lifestyle intervention aimed at modest weight loss (5%-10% of initial weight) and moderate-to-vigorous physical activity reduced the development of diabetes by 58% over a 3-year period. 12 , 23 The DPP was more effective in preventing diabetes than medication (metformin) or placebo. The DPP lifestyle intervention was a goal-based behavioral intervention delivered one-on-one by case-managers or “lifestyle coaches” who were often registered dieticians. The program included strategies to tailor the intervention to the individual participants as well as materials for ethnically diverse populations. Under the well-controlled conditions of the clinical trial, the lifestyle intervention was effective across diverse racial/ethnic groups, including AIANs. The Special Diabetes Program for Indians (SDPI) is a national effort to prevent diabetes among AIANs. The SDPI is a group-based adaptation of the original DPP intervention. In addition to the group format, the SDPI can be delivered by a trained health coach (as opposed to a registered dietician) and includes the adaptations described in Table 1 . The SDPI has been shown to be effective, with a 4% incidence of new diabetes diagnoses per year. 24 Table 1 Changes From DPP to SDPI. Despite the success of the DPP and SDPI, the effectiveness of lifestyle interventions in primary care and community-based settings among racial/ethnic minorities and individuals from low SES backgrounds such as AIANs remains a challenge. Racial/ethnic minority and low-SES populations are more likely to suffer from psychosocial issues that both promote dysmetabolism and interfere with the success of lifestyle interventions. 25-29 Psychosocial issues range from interpersonal stressors such as depression, anxiety, social isolation, lack of cultural identity, and current or past traumas to social stressors like discrimination, acculturation, and family conflict. These stressors can promote dysmetabolism through various mechanisms including neuroendocrine and inflammatory pathways and behavioral coping mechanisms such as overeating and sedentarism. 30-40 They are also likely to interfere with the success of lifestyle interventions. 41 , 42 AIANs have faced warfare and other forms of aggression, diplomatic manipulation, forced assimilation, legal actions, contagious diseases to which they had no immunity, and economic pressure for over 500 years. Noted AIAN scholars have described the effects of these violent strategies within the framework of historical trauma. 43-47 The consequences of these historical atrocities are passed from one generation to another and are found to be intergenerationally cumulative, resulting in compounding health effects across generations. 43-49 These historical factors not only contribute to psychosocial stressors and dysmetabolism, but also create challenges for diabetes-prevention interventions that are framed within a Western, non-Native model of health. Therefore, identifying successful and innovative strategies to interrupt the dysmetabolism pathway in real-world settings may be particularly salient for AIAN communities. While providing significant promise, 50-55 past translations of the DPP to real-world settings also suggest several opportunities for innovation through research. First, studies tend to include a highly selected group of participants that may not generalize adequately to AIAN communities, such as only those meeting criteria for prediabetes when there is evidence suggesting that the DPP may be applicable to additional individuals. 56 Second, there has been limited engagement of patients and other stakeholders in the design, implementation, and analysis of these programs. As a result, the majority of studies focus solely on body mass index (BMI) or other clinical measures without attention to patient-centered outcomes such as quality of life (QOL). Third, the majority of studies do not employ a rigorous randomized controlled trial (RCT) design to evaluate effectiveness and instead rely on less rigorous methods such as preintervention vs postintervention tests that may be subject to bias. Although effective, the SDPI lacks a unified and comprehensive focus on psychosocial conditions, which are critical to successful interruption of the dysmetabolism pathway. It is critical to determine effective strategies for preventing diabetes among these high-risk populations in real-world settings in order to address diabetes disparities in the United States. To fill the research gap pertaining to addressing stressors for diabetes prevention in AIAN communities, this study was designed to develop and test an enhanced DPP for AIAN adults that incorporates culturally sensitive strategies to address mental health concerns and psychosocial stressors in a comparative effectiveness trial. We hypothesized that integrating culturally relevant psychosocial support into the DPP would increase retention and intervention potency by addressing the factors that patients identify as being related to difficulties continuing with the program. Participation of Patients and Other Stakeholders This comparative effectiveness trial is the result of collaboration between an urban AIAN community in Santa Clara County, California, and researchers at Stanford University School of Medicine. The community partner in this study was responsible for initiating the partnership and has been instrumental in all phases of the research process including the design, conduct, and dissemination of findings. Initiation of the Partnership A diabetes health educator in Santa Clara County (Jan J. Vasquez) initiated the collaboration focused on diabetes prevention based on her experience delivering the DPP in her urban community for 7 years before pursuing a collaboration with academic researchers. Ms Vasquez attended trainings on community-based participatory research (CBPR) to better understand how to engage in research to improve health in the community. Through these trainings, Ms Vasquez was connected to the Stanford School of Medicine Office of Community Engagement (OCE), whose mission is to foster community-university partnerships to support health-related research. The OCE played an important role in facilitating the initial development of the partnership by connecting the community to appropriate Stanford researchers, providing seed funding to develop the partnership, and assisting in research capacity–building efforts. The OCE connected the community to 2 researchers (R.S.S. and L.G.R.) with experience in CBPR and behavioral lifestyle interventions for chronic disease prevention. The researchers did not have extensive experience, however, working with AIAN communities. As a first step, the community group and the researchers applied for seed funding from the OCE to develop the partnership. The 12-month award provided funding to conduct research capacity–building activities, develop a community advisory board, and establish a partnership agreement. The OCE provided technical assistance in the research capacity–building activities that included 4 daylong workshops held over sequential Saturdays. Ms Vasquez, the community partner, invited members of the AIAN community who expressed interest in learning more about the research. The workshops were designed and led by all partners of the steering committee including Stanford researchers, Ms Vasquez, and Stanford OCE staff. Workshop attendees included leaders in the community, AIAN elders, adults interested in research and/or diabetes prevention, researchers, and OCE staff. Community leaders and members were selected for their commitment to improving community health. The workshops included team-building activities, an introduction to research, background and history of the local urban AIAN community, the CBPR approach to research, human participant/ethical considerations, IRB training, activities for prioritizing research topics, AIAN culture, and trust-building activities. In alignment with co-learning activities, community members and researchers all provided training during the workshops. Both through self-study and focused discussion with the AIAN community, the academic researchers gained knowledge of AIAN culture, the unique health issues affecting the community, and past mistakes of academic researchers. Following the workshops, attendees established the American Indian Community Action Board (AICAB). The group learned about the role of community advisory boards in the workshop and changed the word advisory to action to reflect the importance of the community taking an active role in all phases of the research process. The AICAB was initially made up of 12 community members, representing diverse facets of the local AIAN community including different tribal groups, age groups, genders, and community members who have temporarily or permanently relocated from their reservations as well as urban community members. AICAB members were encouraged to use their board role to represent the different parts of the community with which they identified. The AICAB decided to meet monthly on a weekday evening to accommodate schedules. In addition, the AICAB continued to seek out additional training in AIAN-specific topics, public health, and research ethics. For example, Maria Yellow Horse Brave Heart and Josie Chase, experts in historical trauma, provided a weekend training on historical trauma. Other trainings focused on a spectrum of prevention and ethical issues. With the AICAB established, one of its first activities was to develop a partnership agreement that outlined the vision, goal, values and philosophy, decision-making process, and roles and responsibilities. The development of the partnership agreement was an opportunity for the researchers to foster trust with an AIAN community that has been historically betrayed by the scientific community. It also provided the opportunity to have up-front discussions about how to run the partnership and served as a resource for problem solving during times of conflict. The partnership agreement can be adapted over time to reflect the changing needs of the partnership. A subsequent activity included deciding on the research priorities. The AICAB and researchers were enthusiastic about focusing on diabetes prevention in their urban community given the high prevalence of diabetes and an interest in incorporating additional strategies to help their community members overcome psychosocial barriers to participating in this type of program. Study Design The AICAB together with the researchers designed the comparative effectiveness trial with input from a scientific advisory board (SAB). The SAB was established by the community partner and consisted of experts in AIAN health and diabetes prevention. The initial decision to focus on diabetes prevention was based on community members prioritizing different health challenges within the community. The partnership developed a governance structure for the trial (see Figure 1 ) that featured a central role for the AICAB as well as a role for a steering committee, the SAB, a data and safety monitoring board (DSMB), program staff, and the National Council of Urban Indian Health (NCUIH), a key stakeholder for dissemination efforts (see Table 2 for project roles). Figure 1 Project Governance. Table 2 Role of Each Group in Project. The partnership collaboratively developed the design, including eligibility criteria, recruitment strategies, selection of comparison groups, primary and secondary outcomes, and dissemination plans. The SAB provided input and guidance on key study design decisions such as the selection of comparison groups. For eligibility criteria, it was important to AICAB members that potential participants could report having “indigenous” identity. The local AIAN community is made up of diverse tribal ancestries including those who are from indigenous groups in the United States, Canada, Mexico, and other countries. In defining the eligibility criteria, the AICAB sought to be inclusive of those for whom the interventions were potentially culturally appealing or appropriate. Recruitment strategies were initially designed to focus on recruiting from a local urban Indian health center. However, because the proportion of American Indian patients in the health clinic was low, recruitment strategies expanded to focus on community-based outreach efforts while keeping the eligibility criteria the same. The AICAB leveraged its own social networks as well. In selecting the comparison groups, the AICAB emphasized the importance of all participants receiving an active intervention because the DPP is an evidence-based intervention and the burden of diabetes is high among AIAN adults. Thus, 2 comparison groups were defined: (1) a standard DPP, which was culturally adapted for AIAN adults; and (2) the enhanced DPP with additional strategies to address mental health challenges. The comparison groups offered the opportunity for all participants to receive an active intervention. The AICAB members also played an active role in selecting the primary and secondary outcomes. The primary outcome of BMI was selected to reflect the importance of preventing diabetes, which was shown to be primarily through weight loss. For secondary outcomes, the partnership obtained a second seed grant to adapt an existing instrument to measure historical trauma in their urban population. Other important secondary outcomes identified by the AICAB and researchers included QOL, behavioral targets, and mental health. Finally, for dissemination plans, the AICAB planned community activities to disseminate results of the research to participants and community members periodically over the course of the study. The community partner and researchers developed a partnership with NCUIH to provide additional avenues for dissemination at the state and local levels. In addition, the AICAB requested training in scientific manuscript interpretation and writing to take a more active role in scientific dissemination. Study Implementation The AICAB continued to play a critical role during the implementation of the study, with the steering committee responsible for day-to-day trial management. The steering committee, which included AICAB members, met weekly to monitor study progress, address problems, and plan for next steps. The AICAB played a central role in the first aim of the study, which focused on developing the enhanced DPP intervention. The AICAB took responsibility for pilot testing 3 proposed enhancements and then deciding how to integrate them into the DPP. In addition, individual AICAB members, depending on their expertise, assumed paid positions on the study. AICAB members were paid as consultants for their work in recruitment, data collection, health coaching, providing ideas for intervention enhancements, and dissemination. In addition, the AICAB reviewed study progress at monthly meetings and participated in addressing problems and planning for next steps. Methods Two-Phase Study Design The primary objective of this research was to determine the best approach for interrupting the dysmetabolism pathway from obesity to diabetes among urban AIANs according to both patient-reported and clinical outcomes using an RCT design. Our community-university partnership jointly determined this question, which is relevant for urban AIANs and their providers because of the high prevalence of diabetes in this community and the cultural congruence of a prevention approach. This study included 2 phases. The objective of phase 1 was to pilot test strategies to address mental health concerns and psychosocial barriers that could be incorporated into the DPP to enhance a standard program that was culturally adapted for AIANs. For example, due to US government policies of termination and assimilation, many urban AIANs experience a loss of cultural connection and family/community ties. Mental health supports were selected based on the need to explore and reconnect participants with their identity as AIANs, their family, and their community. Enhancements incorporated traditional AIAN cultural practice (eg, Talking Circles).The objective of phase 2 was to compare the standard DPP (culturally adapted for AIANs) with the enhanced DPP intervention (addressing mental health concerns) in a comparative effectiveness trial (N = 205). The Stanford University IRBs approved the entire study protocol. All participants provided written informed consent. The study trial protocol was published previously. 57 Phase 1: Develop Enhanced DPP for Urban AIAN Adults The goal of phase 1 research was to pilot test strategies for the enhanced DPP intervention. We undertook a 6-month formative research phase to pilot test culturally relevant strategies including Talking Circles, modified Photovoice, and digital storytelling. The AICAB members pilot tested the 3 strategies. Talking Circles A Talking Circle is a traditional method of group communication where AIAN community members come together to share information, provide social support, and solve community issues. 58 Talking Circles have been successfully used as both a qualitative research method and an intervention strategy for health issues ranging from cervical cancer screening to diabetes management. 14 , 58-66 To pilot test this strategy, the AICAB conducted 4 Talking Circles with different facilitators and settings with a total of 11 participants. Following each Talking Circle, the AICAB met to discuss its potential fit within the DPP intervention as a vehicle for addressing psychosocial barriers to intervention engagement through fostering self-reflection, social support, and community cohesion among AIAN participants. Modified Photovoice We used a modified version of Photovoice, an innovative participatory qualitative method using photography and critical dialogue to identify root causes of health problems and elucidate successful intervention strategies. 67 , 68 The goals of the modified Photovoice was to engage participants to record and reflect on their community's strengths and weaknesses and to promote dialogue about health issues. 67 , 68 Photography can be especially useful for engaging underserved minority groups because photographs taken of their own community elicit a collective emotional response that may uniquely uncover insight into the multilevel factors (eg, food scarcity, social influences, and government policies) that shape diet and physical activity. In addition, photography can aid in bringing out historical, psychosocial, and mental health connections. To pilot test this strategy, the 11 members of the AICAB (5 women and 6 men, age 25-80 years) took part in the modified Photovoice over the course of 10 sessions. Among the participants, the average number of sessions attended was 6 out of 10, ranging from 3 to 10 sessions attended. Following training in use of the camera and photography, participants took pictures in response to 4 prompts: (1) What does wellness and health look like to you? (2) What does healthy and unhealthy love look like to you? (3) What is a personal challenge you have faced and how have you overcome it? (4) What is your meaning? For each prompt, participants took pictures on their own and then came back to the group to share a selection of their pictures with the other participants. Digital storytelling Digital stories are short, first-person narratives that can be presented using traditional or social media formats. The participatory process of developing and sharing digital stories can deeply affect both the person who develops their story as well as viewers, “moving them to reflect on their own experiences, modify their behavior, treat others with greater compassion, speak out about injustice, and become involved in civic and political life.” 69 Developing personal digital narratives is a particularly appealing strategy for urban AIANs because it invokes the traditional cultural practice of oral storytelling. Especially for urban AIANs who may have lost touch with their tribal roots or may suffer from injustices such as discrimination in the urban environment, digital storytelling may form part of a healing process. The goal of the digital storytelling in our formative research was to explore whether adding a digital storytelling component to the DPP would help to address the stress, grief, and depression that result from historical trauma. Ten AICAB members took part in a digital storytelling workshop to pilot test the strategy for potential incorporation into the DPP. The workshop was delivered over the course of 3 days and each participant created their own digital story. Following pilot testing of the 3 enhancements and developing the mental health support component, the AICAB met 8 times to discuss the findings from the pilot study and finalize the strategies to be included in the enhanced DPP. All 3 strategies were selected for inclusion. AICAB members decided that Talking Circles should be conducted immediately after regular sessions and that digital stories and the Photovoice project should be offered as options to participants. In addition to developing these 3 culturally congruent enhancements, a subcommittee of the AICAB developed AIAN-centric mental health strategies for the enhanced DPP, including culturally congruent mental health counseling; celebration of AIAN cultural practices; and destigmatizing individuals' symptoms of depression, anxiety, and other mental health disorders. Providing these forms of mental health support was identified as a means for reducing barriers that exist for AIANs to engage in the successful behavior change needed to prevent diabetes. Participants in the standard DPP intervention were not provided with the culturally congruent enhancements or the AIAN-centric mental health strategies. The facilitator for the standard DPP (who was different from the facilitator for the enhanced DPP group) provided referrals for mental health services available in the community without specific cultural tailoring. For both study groups, it was anticipated that completion of survey questions focused on discrimination and historical trauma might trigger increased participant distress. Protocols were developed and staff were trained to cope with these situations and provide appropriate services and referral as needed. Phase 2: RCT Comparing an Enhanced and Standard DPP The goal of phase 2 was to conduct an RCT to compare the standard DPP (culturally adapted for AIANs) with the enhanced DPP intervention (addressing mental health concerns). For the comparative effectiveness trial, participants were randomly assigned in 5 recruitment cohorts to the enhanced or standard DPP and followed for 12 months. Setting and study participants The study was conducted in a physical activity and recreation center in San Jose, California. The center was accessible by public transportation and offered opportunities for physical activity classes, swimming, and information about other community resources. Adult men and women were recruited (June 1, 2015, to July 31, 2016) through community outreach at local clinics, community-based organizations, retail locations (eg, pharmacies), and schools by using flyers, hosting information sessions, and setting up tables in busy locations and at events in San Jose, Santa Clara County, California. Santa Clara County is home to approximately 25 000 adults who identify as AIAN and is characterized by wide disparities in social determinants of health. 70 , 71 For example, the income gap between the highest earners and the lowest earners in the county is among the top 10 widest in the United States, and the gap appears to be widening over time. Results of this inequality include lack of affordable housing, which can impact food security (ie, “assured access to nutritionally adequate and safe foods without resorting to emergency food supplies, scavenging, stealing, and other coping strategies” 72 ) and other health-related outcomes. 73 Eligibility criteria included self-identification as having indigenous ancestry (ie, descendants of Pre-Columbian peoples) to the United States or the Americas (North, Central, and South America); a BMI between 30 and 55; no diagnosis of type 2 diabetes (defined as fasting glucose <126 mg/dL and no presence of diabetes medications); and at least 1 other criterion for metabolic syndrome: Triglycerides: >150 mg/dL Reduced high-density lipoprotein (HDL) cholesterol: <40 mg/dL (men), <50 mg/dL (women) Blood pressure: >130/80 mm Hg or current treatment with antihypertensives Fasting blood glucose: 100-125 mg/dL This definition is a hybrid of national and international definitions 74 , 75 whose purpose is to identify a population at substantial risk for progression of dysmetabolism, but who have not yet developed diabetes. People with significant psychiatric disorders requiring antipsychotics or multiple medications or medical comorbidities (eg, uncontrolled metabolic disorders, unstable heart disease, heart failure, and ongoing substance abuse) were excluded. Additional exclusions were to protect participant safety (eg, pregnancy) and prevent loss to follow-up (eg, planned relocation). Participants also could self-report information for screening (eg, identification of AIAN ancestry, pregnancy, plans to relocate) at outreach events and by phone. To finalize eligibility, research assistants conducted screening at the study site. Randomization and blinding Eligible participants were randomly assigned in a 1:1 ratio to receive the standard or enhanced DPP. Participants were randomly assigned in blocks to keep the size of the treatment groups similar. The size of each block was randomly selected to be either 2 or 4. To ensure an equal number of men and women in each intervention group, we stratified randomization by sex. The unit of randomization was individual because there was unlikely to be any contamination by factors such as primary care provider or neighborhood. The randomization procedure was executed within the REDCap database by the lifestyle coaches who then informed participants of their treatment assignment. Treatment was identifiable to participants and the lifestyle coaches by design, but the investigators, DSMB, outcome assessors, and the statistician performing the data analysis were blinded to group assignment. A different statistician created a randomization process in REDCap and the study interventionist executed the randomization process. The statistician who created the randomization process was not blinded and did not conduct the data analysis. The outcome assessors and the statistician conducting the data analysis were not given access to the randomization process in REDCap. In addition to restricted access to REDCap, the outcome assessors were based at a different location within the building and made every effort to limit interaction with the interventionists. Treatment groups Both treatment group interventions were based on a version of the DPP that was modified by the SDPI to appeal to AIAN adults ( Table 3 ). Modifications made by the SDPI included consolidating the content into 16 weekly sessions, adding examples and graphics to be appealing to AIAN adults, providing participant incentives, and delivering the sessions in group settings. The DPP intervention is grounded in social cognitive theory 76 and the transtheoretical model of behavior change. 77 , 78 The primary goals are loss of at least 5% of baseline weight and 150 minutes of moderate physical activity per week by 6 months. Although the original DPP trial targeted 7% weight loss, 5% weight loss has been found to be sufficient for prevention of chronic disease and is commonly accepted as the goal. 79 The intervention was delivered by a trained lifestyle coach over 16 weekly group sessions covering information on moderate calorie restriction, physical activity, and proven behavioral strategies. The enhanced group used the same DPP adapted for AIANs as well as the enhancements that were determined during phase 1 of the study. The final enhancements are described in the results for phase 1. Table 3 Comparison of Standard and Enhanced Treatment Groups. Follow-up Follow-up assessments occurred at 6 and 12 months, following the same protocols as baseline. The primary end point was 12 months. Outcome measures All outcome assessors were trained to perform the measurements and interviews per standardized protocols and procedures. Baseline characteristics included sociodemographic characteristics, health behaviors, cardiometabolic risk factors, and psychosocial factors. Sociodemographic characteristics included age, sex, race/ethnicity, income, and educational attainment. Health behaviors included diet, physical activity, alcohol consumption, and sleep habits and quality. Diet and physical activity are secondary outcomes and are described below. Alcohol dependence was assessed using the alcohol consumption questions of the Alcohol Use Disorders Identification Test-Concise (AUDIT-C) 80 and sleep habits and quality were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire. 81 Cardiometabolic risk factors included BMI, weight, waist circumference, blood pressure, fasting blood glucose, and lipid levels. Trained staff conducted anthropometric and blood pressure measurements. 82-84 Measurements of fasting blood glucose and lipid levels were accomplished through point-of-care testing with a Cholestech analyzer (Abbott) to minimize patient burden, maximize access, and provide immediate results. Psychosocial factors included depression, posttraumatic stress disorder (PTSD), food security, and empowerment. Depression and empowerment are secondary outcomes and are described below. PTSD was measured using the 17-item PTSD Checklist–Civilian Version. 85 Food security was measured using the 6-item Short Form of the US Household Food Security Survey and participants were categorized as having “very low food security,” “low food security,” or “high food security.” 72 Our primary outcome was BMI at 12 months. Weight and height were assessed according to standard protocols. 84 Secondary outcomes were QOL, health behaviors (ie, diet and physical activity), and psychosocial factors (ie, depression and empowerment). The QOL outcome was emphasized in the analysis because of the AICAB's interest in this patient-centered outcome. The 12-item Short Form Health Survey (SF-12), which has been used in other studies with AIAN adults, 86 , 87 was used to measure QOL. The SF-12 is a shorter version of the SF-36 that measures functional health and well-being from the patient's point of view. Physical and mental health component scores were computed using the scores of the 12 questions and ranged from 0 to 100, with 0 indicating lowest QOL and 100 indicating highest QOL. Dietary data were collected using a food frequency questionnaire (FFQ) modified to incorporate culturally relevant food choices (eg, corn tortillas and fry bread). 88 Food items were scored on a scale of 1 to 6, with 6 corresponding to the greatest frequency of consumption. Food items on the FFQ were categorized as “healthy,” “unhealthy,” and “undetermined” based on classifications previously determined by Teufel-Shone et al. 88 “Healthy” foods were those recommended for increased intake (eg, green leafy salad). “Unhealthy” foods were recommended for decreased intake (eg, soft drinks), and all remaining foods were “undetermined.” Healthy and unhealthy food scores were obtained by dividing the sum of food items in each category into tertiles, with the third tertile indicating the highest consumption frequency. Undetermined food scores were not used for analysis. Physical activity was measured using the Women's Health Initiative physical activity questionnaire with modifications to reflect the time for recall. 89 Based on the questionnaire, we calculated metabolic equivalent of task (MET) minutes per week, which is a measure of the duration and intensity of physical activity for an individual. Based on a compendium of physical activities, 90 various levels of activity (mild, moderate, and vigorous) are assigned MET values based on how many kilocalories of body weight are expended each minute during a specific activity. The variable is calculated by multiplying the frequency of the activity with minutes of physical activity and MET value of the activity. 89 Depression was measured using the Center for Epidemiological Studies–Depression (CES-D) scale. 91 Empowerment was measured using the Growth and Empowerment Measure, consisting of a 14-item Emotional Empowerment Scale and a 12-item Scenarios scale, designed to measure change in dimensions of empowerment. 92 Potential effect modifiers included sociodemographics such as age, sex, education, employment status, occupation, marital status, household size and income, indigenous ancestry (ie, US indigenous people vs non-US indigenous people), depression, and food insecurity. These effect modifiers were identified by the AICAB and specified a priori. Because the AIAN population in the local area is diverse and those from different ancestral backgrounds may have a different experience of historical trauma, indigenous ancestry (indigenous to the United States and Canada; indigenous to Mexico, Central America, and South America) was identified as a potential effect modifier. Similarly, depression and food insecurity were identified as other psychosocial stressors that may impact effectiveness of the intervention. Finally, to enable examining the association of intervention dose and outcomes, lifestyle coaches recorded each participant's session attendance in REDCap, including if the participant attended in person or via phone, or made up the session after missing it. Statistical analyses Mean and standard deviation for continuous variables and percentage for categorical variables were used for descriptive statistics and session attendance. Standardized difference was used to compare the differences at baseline between the 2 groups. Our study was designed to provide sufficient statistical power to test our primary hypothesis that the enhanced DPP would result in greater weight loss than that with the standard DPP. In the original DPP trial, the average weight loss in the intensive intervention group was 6.9% ± 4.5% after 6 months of follow-up and 4.9% ± 7.4% at the end of the trial (mean follow-up 3.2 years). 93 This percentage weight loss is similar to other studies and greater than that observed in the SDPI evaluation where Jiang et al reported a percentage weight loss of 4.4% following the 16-week program. 24 Based on this literature, we expected a mean weight loss of 4.0% in the standard DPP and 6.5% in the enhanced DPP. To be conservative, we powered the study to be able to detect a difference of 2.0%. Dividing a 2.0% difference (6.0% − 4.0%) by the DPP SD (4.5%) yields a standardized effect size of approximately 0.45. The targeted sample size of 102 participants in each group was designed to provide 80% power to detect an effect size of 0.45 at α = 5% (2-sided) in the primary outcome between enhanced and standard DPP groups, assuming up to a 20% loss to follow-up at 12 months. We performed 4 types of comparisons: (1) between-group differences on primary and secondary outcomes, (2) within-group difference from baseline on primary and secondary outcomes, (3) effect modification, and (4) session attendance and its association with primary outcome. Intention-to-treat (ITT) analyses (ie, statistical tests performed on all randomly assigned participants according to the group to which they were originally assigned) of between-treatment group differences in primary and secondary outcomes tested for treatment-by-time interactions in repeated-measures mixed-effects linear or generalized linear models (ie, 5% weight loss at 6- and 12-month follow-ups). The fixed effects of each model consisted of sex, treatment, time point, and treatment-by-time interaction. The random effects accounted for within-cohort correlation and within-participant correlation. Missing data were handled directly through maximum likelihood estimation in mixed modeling under the missing at random (MAR) assumption. The effects of potential moderators were investigated using mixed-effects linear regression by including an interaction term of treatment and the hypothesized moderator. 94 , 95 The adherence dose effects combining both groups were examined using the same mixed-effect linear model except that treatment was replaced by the number of attended sessions. All analyses were conducted using SAS version 9.4 (SAS Institute Inc.). P < .05 was considered statistically significant. Changes to study There were no changes to the original protocol. 57 Results Phase 1 The AICAB balanced potential for effectiveness with acceptability to participants and feasibility given available resources. In terms of potential for effectiveness and acceptability to participants, the AICAB recognized the important role of tailoring the intervention to each participant's circumstances and wanted to offer choice and flexibility for engaging in the 3 enhancements. Considering the feasibility of the 3 enhancements, the AICAB noted that the Talking Circles were easy to implement with few resources while the digital storytelling workshop required the most resources and personnel; the Photovoice project required a moderate level of resources. Balancing these factors, the AICAB decided to add 3 Talking Circles to the original 16-week intervention at the beginning (session 3), middle (session 8), and end (session 15) and the option to engage in a digital storytelling workshop or a Photovoice project. The choice to engage in digital storytelling or Photovoice would be driven by the participant with input from the lifestyle coach. In addition to these strategies, the AICAB developed a system to provide additional mental health support. A licensed clinical social worker (LCSW) with significant AIAN-community experience as well as being Native herself was hired to provide mental health support for those in the enhanced DPP group. The LCSW met with the health coach weekly to discuss participants who were facing mental health challenges and develop a plan. The plan could include behavioral strategies implemented by the health coach, referral to local services, or one-on-one meeting(s) with the LCSW. Phase 2 Study Participants and Baseline Characteristics Of the 1326 potential participants referred through community-based outreach, 908 completed initial screening and 418 people did not. Of the 418 who did not complete the screening, staff attempted to contact 237 people who were nonresponsive, and reached 158 who were not ready to commit at that time and asked to be contacted in the future; 23 people were not called because the enrollment target had been met. Of the 908 who completed initial screening, 379 were not eligible (primarily not reporting indigenous ancestry and/or reporting a current diagnosis of diabetes) or declined participation at this stage, 133 were eligible but not interested, 46 needed physician approval, and 350 completed the subsequent clinical screening step. Among the 350 who completed clinical screening, 278 were eligible or needed physician approval, of whom 213 completed the baseline visit. Six people who were eligible and completed the baseline visit decided they did not want to participate due to competing priorities, including the care of young children, and were not randomly assigned. This process yielded the sample size of 207 eligible and consenting participants. Two participants were excluded postrandomization due to safety concerns for the study staff. Of the 205 remaining randomly assigned participants, 3 dropped out of the study before the 6-month assessment due to time issues (n = 2) or loss of interest (n = 1). Besides the 3 dropouts, 45 participants were lost to follow-up at 6 months and 27 at 12 months, because they could not be contacted by phone, email, or US Mail. As a result, 157 participants (76%) were assessed at 6 months and 175 (85%) at 12 months ( Figure 2 ). Figure 2 CONSORT Diagram of an RCT Comparing 2 Approaches to Diabetes Prevention Among AIAN Adults in San Jose, California (June 1, 2015-July 31, 2016). Participants were middle-aged (mean [SD], 52.0 years [13.3]), mostly female (78.7%), and obese (BMI, 37.5 [6.6] for men, 37.2 [6.0] for women), with indigenous ancestry from multiple regions ( Table 4 ). At baseline, 14% of participants were hazardous drinkers. Participants had a mean (SD) sleep disturbance T-score of 52.7 (3.8) and a sleep-related impairment T-score of 53.6 (7.4), representing worse sleep quality and daytime sleepiness than the average population. In addition, 34% of participants reported symptoms consistent with depression (CES-D score ≥16). Their mean (SD) systolic blood pressure was 123.6 (17.1) mm Hg, diastolic blood pressure was 77.9 (11.9) mm Hg, fasting blood glucose was 100.7 (10.7) mg/dL, HDL cholesterol was 49.9 (14.7) mg/dL, low-density lipoprotein (LDL) cholesterol was 101.5 (29.1) mg/dL, triglycerides were 142.2 (82.1) mg/dL, and total cholesterol was 180.3 (39.0) mg/dL. Table 4 Baseline Participant Characteristics Overall and by Group in San Jose, California. Primary and Secondary Outcomes At 6 months, the participants in the enhanced intervention had a mean (SE) BMI of 37.5 (0.7), compared with 36.3 (0.7) for participants in the standard intervention. At 12 months, participants in the enhanced intervention had a mean (SE) BMI of 37.6 (0.7), and the corresponding outcome for the participants in the standard intervention was 36.4 (0.7). The mean change in BMI from baseline in the groups did not differ significantly at either follow-up time point ( Table 5 ). Table 5 Estimated Means and SEs for Baseline, and Changes From Baseline for Primary and Secondary Outcomes by Time Points, in the ITT Population in San Jose, California . The 2 treatment groups did not differ significantly in secondary outcomes (QOL, depression, diet, physical activity MET minutes, and empowerment) at baseline, 6, and 12 months, except for alcohol consumption at 6 months and unhealthy food consumption frequency at 12 months. At 6 months, participants in the enhanced intervention decreased their AUDIT-C scores (ie, risk of alcohol dependence) more than participants in the standard intervention (−0.3 [95% CI, −0.6 to 0] vs 0.1 [95% CI, −0.2 to 0.4]; P < .05). At 12 months, participants in the standard intervention decreased their consumption of unhealthy foods more than participants in the enhanced intervention (−4.6 [95% CI, −6.8 to −2.5] vs −0.7 [95% CI, −2.9 to 1.4]; P < .05). Figure 3 shows within-group differences in BMI and QOL over time. Both treatment groups had a significantly lower BMI at 6 months compared with baseline (−0.3 [95% CI, −0.7 to −0.02]; P = .04 for the enhanced intervention vs −0.7 [95% CI, −1.0 to −0.4]; P < .0001 for the standard intervention); however, only participants in the standard intervention had a significantly lower BMI at 12 months than at baseline (−0.5 [95% CI, −1.0 to −0.1]; P = .02). The mean (95% CI) percentage of participants with 5% weight loss was 10% (4%-23%) and 21% (10%-37%) for the enhanced and standard intervention groups, respectively, at 6 months and 18% (9%-33%) and 24% (12%-41%), respectively, at 12 months (not significant at each time point for either treatment group). Participants in the enhanced intervention had a significantly higher SF-12 physical component score at both 6 months (3.1 [95% CI, 1.0-5.2]; P = .004) and 12 months (2.9 [95% CI, 0.7-5.2]; P = .01) than at baseline, while participants in the standard intervention only had a significantly higher SF-12 physical component score at 6 months (3.0 [95% CI, 0.9-5.0]; P = .005). SF-12 mental component score did not change significantly at each follow-up time point within either treatment group. Figure 3 Estimated Means and 95% CIs for Primary Outcome of BMI and Secondary Outcome of QOL by Group in All Participants . Effect Modification Effect modification analysis showed that baseline sociodemographics, indigenous ancestry (ie, US indigenous people vs non-US indigenous people), depression, and food insecurity did not modify the intervention effects on the primary outcome (Supplemental Table 1). Figure 4 shows estimated means and standard errors for BMI, percentage weight change, and QOL by treatment group among US indigenous people only. Among US indigenous people, only participants in the standard intervention had a significantly lower BMI at 6 months (mean change, −0.8 [95% CI, −1.2 to −0.4]; P < .001) and 12 months (mean change, −0.3 [95% CI, −1.3 to 0.1]; P = .01) than at baseline. BMI at both follow-up time points did not differ from baseline among participants in the enhanced intervention. Mean percentage (95% CI) of participants with 5% weight loss was 7% (2%-22%) and 15% (6%-35%) for enhanced and standard intervention groups, respectively, at 6 months and 16% (6%-36%) and 23% (9%-46%), respectively, at 12 months (not significant at each time point for either treatment group). Participants in the enhanced intervention had a significantly higher SF-12 physical component score at 6 months (mean, 4.4 [95% CI, 2.0-6.9]; P < .001) and 12 months (mean, 6.2 [95% CI, 3.1-9.2]; P < .001) than at baseline, while the SF-12 physical component score at both follow-up time points did not differ from baseline among participants in the standard intervention. SF-12 mental component scores did not change significantly at each follow-up time point within either treatment group. Figure 4 Estimated Means and 95% CIs for Primary Outcome of BMI and Secondary Outcome of QOL by Group in US Indigenous Participants . Session Attendance Out of a total of 16 weekly sessions, the mean (SD) number of sessions attended that were recorded by the lifestyle coach was 9.5 (5.7) for the enhanced intervention group and 9.0 (5.3) for the standard intervention group. Of the enhanced intervention participants, 79%, 62%, and 46% completed at least 4, 8, and 12 sessions, respectively, and the corresponding numbers were 80%, 62%, and 38%, respectively, among the standard intervention participants. In each treatment group, there were 7 participants who did not attend any sessions. Among enhanced intervention participants, number (%) of participants who attended 0, 1, 2, 3, and 4 Photovoice sessions (out of a total of 4) were 21 (20%), 19 (18%), 13 (13%), 13 (13%), and 37 (36%), respectively; and the number (%) of participants who attended 0, 1, 2, and 3 Talking Circles (out of a total of 3) were 50 (49%), 19 (18%), 15 (15%), and 19 (18%), respectively. Only 1 participant attended both digital storytelling sessions. Repeated-measures mixed-effects linear models among all participants combined indicated that higher adherence was significantly associated with a greater decrease in BMI ( Figure 5 ). Mean coefficient (95% CI) for the slope of the linear line was −0.06 (95% CI, −0.10 to −0.01; P = .01) and −0.07 (95% CI, −0.13 to −0.01; P = .03) at 6 months and 12 months, respectively. Figure 5 Mean BMI Change With Increase of Group Session Attendance Among All Participants . Discussion Context for Study Results This comparative effectiveness trial of 2 approaches to diabetes prevention among AIAN adults at risk for diabetes in an urban area was initiated by community members and implemented by a community-university research partnership. The study resulted in several important findings. First, community members successfully pilot tested 3 enhancements and developed additional mental health support to supplement the standard DPP for urban AIAN adults. Second, contrary to our hypothesis, the standard and enhanced interventions did not significantly differ in any of the primary or secondary outcomes except for dietary intake, where the standard group decreased their consumption of unhealthy food more than the enhanced group. Third, some participants were successful in reducing their BMI and improving their QOL, which has implications for future research directions. Additionally, the high level of involvement of the AICAB in the study promoted successful recruitment, retention, and intervention engagement. The AICAB pilot tested 3 strategies to enhance the DPP to address psychosocial stressors among AIAN adults, including historical trauma that was posited to lead to dysmetabolism and interfere with engagement in diabetes prevention. In pilot testing Talking Circles, Photovoice, and digital storytelling, the AICAB determined that the Talking Circles were the most feasible to include with the DPP. Although the AICAB thought Photovoice and digital storytelling would be acceptable for participants, they also believed they would be more challenging to include as part of a DPP. Nevertheless, the AICAB members, many of whom had previously taken part in a DPP intervention, understood that participants would differ in their level of engagement, and thus sought to offer the opportunity for participants to engage in all 3 of the enhancements. Thus, having the AICAB take the lead in developing and pilot testing the enhancements promoted patient stakeholder engagement and patient centeredness. This is a best practice for addressing disparities in complex health conditions such as diabetes. 96 Future efforts to enhance or tailor the DPP for urban AIAN populations could use an adaptation framework such as the ADAPT-ITT framework 97 or a similar 4-stage method 98 that involves a systematic review of the intervention content and in-depth adaptation of core constructs within the intervention. Alternatively, the DPP could be combined with a second evidence-based approach to address psychosocial barriers, including historical trauma. A barrier to this approach is the limited evidence on evidence-based practices for addressing the diverse psychosocial barriers that urban AIAN adults face. Despite the fact that the community partner was integrally involved in developing the enhanced intervention, the trial demonstrated that participants in the enhanced and standard interventions did not differ in primary or secondary outcomes, with the exception of diet. There are several possible explanations for this finding. First, it may not be necessary to address historical trauma in the context of diabetes prevention for urban AIANs. Documentation among AIANs residing on reservations suggests that historical trauma as measured by the Historical Loss Scale and the Historical Loss Associated Symptoms Scale is common. 43 For example, among 143 indigenous adults recruited from 2 reservations in the American Midwest, the historical losses most commonly thought about weekly, daily, or several times a day included “Loss of respect by our children and grandchildren for elders” (65%); “The losses from the effects of alcoholism on our people” (64%); “Losing our traditional spiritual ways” (55%); “Loss of our people through early death” (55%); and “Loss of respect by our children for traditional ways” (53%). In contrast, among trial participants in an urban area who reported indigenous ancestry from the United States and Canada, the top 5 historical losses thought about at least weekly included “Loss of respect by our children and grandchildren for elders” (20%); “Loss of respect by our children for traditional ways” (19%); “The loss of our land due to the Spanish conquest or colonization” (14%); “The losses from the effects of alcoholism on our people” (12%); and “The losses from the effects of drugs on our people” (12%). The relatively low rates of historical losses suggest that addressing historical trauma in this urban population for the purposes of augmenting the effectiveness of diabetes prevention was not important. A second and related explanation is that addressing barriers other than historical trauma is potentially more important. Both groups of the intervention addressed numerous other barriers that indigenous populations commonly face for successful diabetes prevention such as lack of transportation, competing priorities of work and caretaking, and lack of safe places for physical activity. Strategies to address these barriers were provided to participants in both groups and included hosting the intervention at convenient times and in a location accessible by public transport, providing access to public transportation, membership at a gym (either on site or in a location convenient to the participant), and providing incentives such as a healthy meal during the class and comfortable athletic shoes for each participant. It is possible that addressing these barriers was sufficient for this urban population and that additional strategies to address historical trauma were not needed. Lack of access to healthy food outside of the group sessions was an important barrier for some participants, as noted by a high level of food insecurity in this population, and was also a difficult barrier to address. Alternatively, it is possible that the 3 strategies developed to address historical trauma were not sufficient to be effective. Historical trauma is a complex issue that may require more in-depth or long-term intervention than is feasible in the context of a DPP intervention. 45 , 99-101 Finally, it is possible that the strategies were adequate but that engagement in those strategies fell short of what was needed for effectiveness. Approximately one-third (36%) attended all 4 Photovoice sessions and one-fifth (18%) attended all 3 Talking Circles. Only 1 person completed the digital storytelling. Intervention staff and AICAB members put forth considerable effort in the form of outreach and engagement with participants in the enhanced intervention. However, it is possible that participants did not receive a sufficient dose of these historical trauma-directed interventions. Despite the fact that the groups did not differ according to the primary outcome, participants in both groups made significant improvements in BMI and QOL compared with their baseline levels. Compared with baseline, participants randomly assigned to the enhanced intervention group had a significantly lower BMI at 6 but not at 12 months and significantly higher QOL at 6 and 12 months. Compared with baseline, participants randomly assigned to the standard intervention group had significantly lower BMI at 6 and 12 months and significantly higher QOL at 6 months but not 12 months. Overall, 18% of participants in the enhanced intervention and 24% in the standard intervention had lost at least 5% of their weight at 12 months. Systematic reviews of studies that tested implementations of DPPs found that between 20% and 64% of participants achieved 5% weight loss. 102 The finding on weight loss from this study is in the lowest end of this range, which may have to do with the challenge of addressing psychosocial barriers in this group. Our study found that increased attendance was associated with greater weight loss at both 6 and 12 months. This is consistent with other studies that have documented the benefit of attending more sessions (which could also mean that good attendance is a marker for personal characteristics that lead to adherence to exercise and diet). The importance of this finding is reflected in policies such as from the Centers for Disease Control and Prevention (CDC) DPP, which provides recognition to DPP providers. To achieve recognition, the CDC requires that at least 60% of participants attend at least 9 sessions during months 1-6 and at least 60% of participants attend at least 3 sessions in months 7 to 12. However, it is important to recognize that the number of sessions needed to reach intervention targets for weight loss and physical activity may differ by individual depending on numerous factors such as baseline characteristics (eg, weight, fitness level, comorbidities) and social determinants (eg, access to food, social support). Generalizability of the Findings The results of this study are likely to generalize to other urban AIAN groups in the United States, and especially groups in California with a high proportion who also identify as Hispanic. California is home to the largest indigenous population in the United States; thus, these findings have widespread generalizability. The findings of this study may not generalize to reservation settings or urban AIAN communities in other areas of the United States. However, to the extent that other AIAN communities in urban and rural settings in the United States face psychosocial barriers similar to this population, the findings may be applicable. Implementation of Study Results Increasing engagement in the intervention is a significant challenge in real-world settings and especially for low-income and racial/ethnic minority populations that face numerous barriers. As previously mentioned, both groups of the intervention included numerous strategies to address common barriers to engagement in the intervention. Nevertheless, less than half of the participants attended 75% of the intervention. Additional strategies are needed to augment session attendance in order to increase effectiveness. Technology-supported strategies, such as telehealth, may be a strategy to increase attendance that would allow participants to engage in the intervention remotely. Health coaches in both intervention groups encouraged participants to attend sessions via video conference or phone if they were unable to attend in person. Combining traditional DPP interventions with efforts to modify social, physical, and policy environments that promote diabetes may be an additional strategy to augment engagement and effectiveness. Participants from low-income and racial/ethnic minority backgrounds may feel overwhelmed with implementing the individual-level behavior changes that are necessary to prevent diabetes within social and physical environments that promote the opposite. Engaging people in participatory efforts to modify their environments to make healthy choices their default choices may be helpful. Finally, meaningful linkages with the primary care system may be leveraged to increase adherence to the intervention. For AIAN populations, culturally competent primary care is critical for this strategy to be effective. Primary care providers and staff can help to address barriers to engagement such as depression and other comorbidities and can provide reinforcement of the importance of attending the intervention. In addition to findings from the pilot testing and RCT, this study demonstrated the feasibility of in-depth community engagement through establishing, training, and sustaining an active community advisory board. The community partner in this trial initiated the research partnership and took an active role in the design, implementation, and dissemination of research findings. As opposed to having a limited advisory role, the AICAB played a central role in all key decisions regarding the study. As a result of this involvement, the community partner increased their research capacity and the research partner increased their knowledge of AIAN health and their ability to do research in partnership with this population. Co-learning was a central aspect of the engagement process throughout the course of the project. The community partner learned about study design, implementation of a trial, data analysis, and dissemination of results. The research partner learned about historical trauma, AIAN culture, AIAN conceptualizations of health and well-being, and historically rooted concerns related to research ethics. At the same time, there were numerous challenges related to community engagement from the community and researcher perspectives. For the community, it was challenging to respond to the changing needs and priorities of the community within the confines of an RCT and contracts with a large academic institution. For researchers, it was challenging to implement a rigorous study given the instability and limited infrastructure of community organizations and shifting priorities and politics within the community. A strong partnership-development process and a partnership agreement were essential for navigating conflicts that arose as a result of these challenges. Nevertheless, the benefits of the engagement far outweighed the challenges. Engagement efforts were critical for designing a study that was acceptable and feasible in a real-world community setting, for recruiting a difficult-to-reach population that has significant concerns about participating in research, and for disseminating trial results that were not what the community was expecting. Without community engagement, these significant aspects of the study could not have been successfully navigated. Subpopulation Considerations The findings for participants who reported indigenous ancestry from the United States and Canada only were similar to findings for those with mixed ancestry. Similarly, we did not detect any significant differences between those who did and did not report depression at baseline or between those who did and did not report food insecurity (see Supplemental Table 1 in the Appendix ). It is possible that the sample size was not sufficient to detect a significant moderating effect of these factors on the effectiveness of the intervention. If there was truly no significant difference in intervention effectiveness according to these a priori–defined effect modifiers, it either suggests that efforts to tailor interventions for these characteristics were already incorporated or that additional tailoring efforts were not needed. Study Limitations There are several important limitations to note for contextualizing the findings. First, the study population represented the heterogeneous indigenous population of the local area, primarily with indigenous ancestry from the United States and Mexico. While this was important to the community, it also resulted in a potential limitation. There are significant demographic, social, and behavioral differences between those who report indigenous ancestry from these 2 regions that may be related to intervention effectiveness. Although the subgroup analyses did not demonstrate differential effectiveness, it is possible that the sample size was too small to detect subgroup differences. Second, although the sample size accounted for attrition, it is possible that we did not have sufficient power to detect a difference due to loss to follow-up. The study staff implemented numerous strategies to augment retention, yet 14% of the standard intervention group and 16% of the enhanced intervention group were not able to provide data at the 12-month time point. Third, the reasons for loss to follow-up remain unknown, making it impossible to know whether participants did not return for follow-up visits for reasons that may be related to the outcome (eg, weight gain). Thus, it is challenging to test whether the MAR assumption of the mixed-effects linear models holds. 103 However, the loss to follow-up was not different between treatment groups, so the effect estimates are unlikely to be biased. Future Research Future directions for diabetes prevention among urban AIAN populations should focus on how to increase engagement and adherence to the intervention given existing barriers. These strategies will likely need to go beyond overcoming barriers related to competing priorities of work/caregiving, transportation, culture, and access to healthy food and safe places for physical activity, because many of these barriers have been addressed in this study and others. 24 Innovative strategies using technology or participatory methods to address more upstream barriers are potential avenues to pursue. Conclusions This project demonstrated a community partner's successful initiation and implementation of a study to better understand how to prevent diabetes among high-risk AIAN adults residing in an urban area. The findings show that adding strategies to address historical trauma to a standard DPP intervention that was culturally adapted for AIAN adults was not more effective than the standard intervention alone and was potentially less effective than the standard intervention in decreasing consumption of unhealthy foods. Explanations for these findings may relate to the closely aligned design of the 2 interventions, the impact of the enhancements, or shortcomings in adherence. Overall, however, participants who attended more sessions lost more weight, which may reflect the importance of intervention adherence, as other studies have shown. 104-106 References 1. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA. 2012;307(5):491-497. [ PubMed : 22253363 ] 2. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet. 2011;378(9793):815-825. [ PubMed : 21872750 ] 3. Centers for Disease Control and Prevention. National diabetes fact sheet, 2011. 2011. Accessed March 5, 2018. https://www ​.cdc.gov/diabetes ​/basics/quick-facts.html 4. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021-1029. [ PubMed : 26348752 ] 5. Liao Y, Bang D, Cosgrove S, et al. Surveillance of health status in minority communities—Racial and Ethnic Approaches to Community Health Across the U.S. (REACH U.S.) Risk Factor Survey, United States, 2009. MMWR Surveill Summ. 2011;60(6):1-44. [ PubMed : 21597458 ] 6. McBean AM, Li S, Gilbertson DT, Collins AJ. Differences in diabetes prevalence, incidence, and mortality among the elderly of four racial/ethnic groups: Whites, Blacks, Hispanics, and Asians. Diabetes Care. 2004;27(10):2317-2324. [ PubMed : 15451894 ] 7. Boykin S, Diez-Roux AV, Carnethon M, Shrager S, Ni H, Whitt-Glover M. Racial/ethnic heterogeneity in the socioeconomic patterning of CVD risk factors: in the United States: the multi-ethnic study of atherosclerosis. J Health Care Poor Underserved. 2011;22(1):111-127. [ PMC free article : PMC3312013 ] [ PubMed : 21317510 ] 8. Kurian AK, Cardarelli KM. Racial and ethnic differences in cardiovascular disease risk factors: a systematic review. Ethn Dis. 2007;17(1):143-152. [ PubMed : 17274224 ] 9. Lantz PM, Golberstein E, House JS, Morenoff J. Socioeconomic and behavioral risk factors for mortality in a national 19-year prospective study of U.S. adults. Soc Sci Med. 2010;70(10):1558-1566. [ PMC free article : PMC3337768 ] [ PubMed : 20226579 ] 10. U.S. Census Bureau. American Indian and Alaska Native Heritage Month: November 2017. Published October 6, 2017. Accessed October 5, 2018 [Link no longer active]. https://www ​.census.gov ​/newsroom/facts-for-features ​/2017/aian-month.html 11. King M, Smith A, Gracey M. Indigenous health part 2: the underlying causes of the health gap. Lancet. 2009;374(9683):76-85. [ PubMed : 19577696 ] 12. Knowler WC, Pettitt DJ, Saad MF, Bennett PH. Diabetes mellitus in the Pima Indians: incidence, risk factors and pathogenesis. Diabetes Metab Rev. 1990;6(1):1-27. [ PubMed : 2192853 ] 13. Gracey M, King M. Indigenous health part 1: determinants and disease patterns. Lancet. 2009;374(9683):65-75. [ PubMed : 19577695 ] 14. Castro S, O'Toole M, Brownson C, Plessel K, Schauben L. A diabetes self-management program designed for urban American Indians. Prev Chronic Dis. 2009;6(4):A131. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC2774645/ [ PMC free article : PMC2774645 ] [ PubMed : 19755007 ] 15. Gittelsohn J, Rowan M. Preventing diabetes and obesity in American Indian communities: the potential of environmental interventions. Am J Clin Nutr. 2011;93(5):1179S-1183S. [ PubMed : 21411614 ] 16. Indian Health Service. Healthy weight for life: a vision for healthy weight across the lifespan of American Indians and Alaska Natives, actions for health care team and leaders. 2011. Accessed March 6, 2018 [Link no longer active]. https://www ​.ihs.gov/healthyweight ​/includes ​/themes/newihstheme ​/display_objects/documents ​/HW4L_TeamsLeaders.pdf 17. Kuller LH. Diabetes in American Indians. Reflections and future directions. Diabetes Care. 1993;16(1):380-382. [ PubMed : 8422818 ] 18. Mohatt GV, Plaetke R, Klejka J, et al. The Center for Alaska Native Health Research Study: a community-based participatory research study of obesity and chronic disease-related protective and risk factors. Int J Circumpolar Health. 2007;66(1):8-18. [ PubMed : 17451130 ] 19. Sequist TD, Cullen T, Acton KJ. Indian Health Service innovations have helped reduce health disparities affecting American Indian and Alaska native people. Health Aff (Millwood). 2011;30(10):1965-1973. [ PubMed : 21976341 ] 20. Schiller JS, Lucas JW, Ward BW, Peregoy JA. Summary health statistics for U.S. adults: National Health Interview Survey, 2010. Vital Health Stat 10. 2012(252):1-207. [ PubMed : 22834228 ] 21. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey Data. Updated November 5, 2019. https://www ​.cdc.gov/brfss/index.html 22. US Office of Minority Health. Mental and Behavioral Health – American Indians/Alaska Natives. US Department of Health and Human Services. 2012. Updated September 25, 2019. https://www ​.minorityhealth ​.hhs.gov/omh/browse ​.aspx?lvl=4&lvlid=39 23. Diabetes Prevention Program Research Group, Knowler WC, Fowler SE, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677-1686. [ PMC free article : PMC3135022 ] [ PubMed : 19878986 ] 24. Jiang L, Manson SM, Beals J, et al. Translating the Diabetes Prevention Program into American Indian and Alaska Native communities: results from the Special Diabetes Program for Indians Diabetes Prevention demonstration project. Diabetes Care. 2013;36(7):2027-2034. [ PMC free article : PMC3687272 ] [ PubMed : 23275375 ] 25. Centers for Disease Control and Prevention. Adverse childhood experiences reported by adults—five states, 2009. MMWR Morb Mortal Wkly Rep. 2010;59(49):1609-1613. [ PubMed : 21160456 ] 26. Ghafoori B, Barragan B, Tohidian N, Palinkas L. Racial and ethnic differences in symptom severity of PTSD, GAD, and depression in trauma-exposed, urban, treatment-seeking adults. J Trauma Stress. 2012;25(1):106-110. [ PMC free article : PMC3599779 ] [ PubMed : 22354513 ] 27. Williams DR, Mohammed SA. Discrimination and racial disparities in health: evidence and needed research. J Behav Med. 2009;32(1):20-47. [ PMC free article : PMC2821669 ] [ PubMed : 19030981 ] 28. Pascoe EA, Smart Richman L. Perceived discrimination and health: a meta-analytic review. Psychol Bull. 2009;135(4):531-554. [ PMC free article : PMC2747726 ] [ PubMed : 19586161 ] 29. Beckie TM. A systematic review of allostatic load, health, and health disparities. Biol Res Nurs. 2012;14(4):311-346. [ PubMed : 23007870 ] 30. Aldana SG, Sutton LD, Jacobson BH, Quirk MG. Relationships between leisure time physical activity and perceived stress. Percept Mot Skills. 1996;82(1):315-321. [ PubMed : 8668498 ] 31. Gibson EL. Emotional influences on food choice: sensory, physiological and psychological pathways. Physiol Behav. 2006;89(1):53-61. [ PubMed : 16545403 ] 32. Schulz AJ, House JS, Israel BA, et al. Relational pathways between socioeconomic position and cardiovascular risk in a multiethnic urban sample: complexities and their implications for improving health in economically disadvantaged populations. J Epidemiol Community Health. 2008;62(7):638-646. [ PMC free article : PMC2668209 ] [ PubMed : 18559448 ] 33. Golden SH, Lazo M, Carnethon M, et al. Examining a bidirectional association between depressive symptoms and diabetes. JAMA. 2008;299(23):2751-2759. [ PMC free article : PMC2648841 ] [ PubMed : 18560002 ] 34. Winkleby MA, Cubbin C, Ahn DK, Kraemer HC. Pathways by which SES and ethnicity influence cardiovascular disease risk factors. Ann N Y Acad Sci. 1999;896:191-209. [ PubMed : 10681898 ] 35. Dong M, Giles WH, Felitti VJ, et al. Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation. 2004;110(13):1761-1766. [ PubMed : 15381652 ] 36. Harding JL, Backholer K, Williams ED, et al. Psychosocial stress is positively associated with body mass index gain over 5 years: evidence from the longitudinal AusDiab study. Obesity (Silver Spring). 2014;22(1):277-286. [ PubMed : 23512679 ] 37. Fisher EB, Chan JC, Nan H, Sartorius N, Oldenburg B. Co-occurrence of diabetes and depression: conceptual considerations for an emerging global health challenge. J Affect Disord. 2012;142 Suppl:S56-S66. [ PubMed : 23062858 ] 38. Ma Y, Balasubramanian R, Pagoto SL, et al. Relations of depressive symptoms and antidepressant use to body mass index and selected biomarkers for diabetes and cardiovascular disease. Am J Public Health. 2013;103(8):e34-e43. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC3791588/ [ PMC free article : PMC3791588 ] [ PubMed : 23763394 ] 39. Mezuk B, Rafferty JA, Kershaw KN, et al. Reconsidering the role of social disadvantage in physical and mental health: stressful life events, health behaviors, race, and depression. Am J Epidemiol. 2010;172(11):1238-1249. [ PMC free article : PMC3025628 ] [ PubMed : 20884682 ] 40. Scott KM, Von Korff M, Angermeyer MC, et al. Association of childhood adversities and early-onset mental disorders with adult-onset chronic physical conditions. Arch Gen Psychiatry. 2011;68(8):838-844. [ PMC free article : PMC3402030 ] [ PubMed : 21810647 ] 41. Delahanty LM, Conroy MB, Nathan DM; Diabetes Prevention Program Research Group. Psychological predictors of physical activity in the Diabetes Prevention Program. J Am Diet Assoc. 2006;106(5):698-705. [ PMC free article : PMC2495077 ] [ PubMed : 16647327 ] 42. Delahanty LM, Peyrot M, Shrader PJ, et al. Pretreatment, psychological, and behavioral predictors of weight outcomes among lifestyle intervention participants in the Diabetes Prevention Program (DPP). Diabetes Care. 2013;36(1):34-40. [ PMC free article : PMC3526204 ] [ PubMed : 23129133 ] 43. Whitbeck LB, Adams GW, Hoyt DR, Chen X. Conceptualizing and measuring historical trauma among American Indian people. Am J Community Psychol. 2004;33(3-4):119-130. [ PubMed : 15212173 ] 44. Evans-Campbell T. Historical trauma in American Indian/Native Alaska communities: a multilevel framework for exploring impacts on individuals, families, and communities. J Interpers Violence. 2008;23(3):316-338. [ PubMed : 18245571 ] 45. Brave Heart MY, Chase J, Elkins J, Altschul DB. Historical trauma among Indigenous Peoples of the Americas: concepts, research, and clinical considerations. J Psychoactive Drugs. 2011;43(4):282-290. [ PubMed : 22400458 ] 46. Brave Heart MY. The historical trauma response among natives and its relationship with substance abuse: a Lakota illustration. J Psychoactive Drugs. 2003;35(1):7-13. [ PubMed : 12733753 ] 47. Brave Heart MY. Gender differences in the historical trauma response among the Lakota. J Health Soc Policy. 1999;10(4):1-21. [ PubMed : 10538183 ] 48. Brave Heart MY, DeBruyn LM. The American Indian Holocaust: healing historical unresolved grief. Am Indian Alaska Native Ment Health Res. 1998;8(2):56-78. [ PubMed : 9842066 ] 49. Myhra LL. “It runs in the family”: intergenerational transmission of historical trauma among urban American Indians and Alaska Natives in culturally specific sobriety maintenance programs. Am Indian Alaska Native Ment Health Res. 2011;18(2):17-40. [ PubMed : 22302280 ] 50. Piatt GA, Seidel MC, Chen HY, Powell RO, Zgibor JC. Two-year results of translating the diabetes prevention program into an urban, underserved community. Diabetes Educ. 2012;38(6):798-804. [ PubMed : 22968220 ] 51. Saaristo T, Moilanen L, Korpi-Hyovalti E, et al. Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D). Diabetes Care. 2010;33(10):2146-2151. [ PMC free article : PMC2945150 ] [ PubMed : 20664020 ] 52. Katula JA, Vitolins MZ, Rosenberger EL, et al. One-year results of a community-based translation of the Diabetes Prevention Program: Healthy-Living Partnerships to Prevent Diabetes (HELP PD) Project. Diabetes Care. 2011;34(7):1451-1457. [ PMC free article : PMC3120203 ] [ PubMed : 21593290 ] 53. Ma J, Yank V, Xiao L, et al. Translating the Diabetes Prevention Program lifestyle intervention for weight loss into primary care: a randomized trial. JAMA Intern Med. 2013;173(2):113-121. [ PMC free article : PMC3856315 ] [ PubMed : 23229846 ] 54. Piatt GA, Seidel MC, Powell RO, Zgibor JC. Comparative effectiveness of lifestyle intervention efforts in the community: results of the Rethinking Eating and ACTivity (REACT) study. Diabetes Care. 2013;36(2):202-209. [ PMC free article : PMC3554313 ] [ PubMed : 22966092 ] 55. Jackson L. Translating the Diabetes Prevention Program into practice: a review of community interventions. Diabetes Educ. 2009;35(2):309-320. [ PubMed : 19321809 ] 56. Pagoto SL, Kantor L, Bodenlos JS, Gitkind M, Ma Y. Translating the Diabetes Prevention Program into a hospital-based weight loss program. Health Psychol. 2008;27(1S):S91-S98. [ PubMed : 18248110 ] 57. Rosas LG, Vasquez JJ, Naderi R, et al. Development and evaluation of an enhanced Diabetes Prevention Program with psychosocial support for urban American Indians and Alaska natives: a randomized controlled trial. Contemp Clin Trials. 2016;50:28-36. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC6691492/ [ PMC free article : PMC6691492 ] [ PubMed : 27381232 ] 58. Hodge FS, Fredericks L, Rodriguez B. American Indian women's talking circle. A cervical cancer screening and prevention project. Cancer. 1996;78(7 Suppl):1592-1597. [ PubMed : 8839577 ] 59. Becker SA, Affonso DD, Beard MB. Talking circles: Northern Plains tribes American Indian women's views of cancer as a health issue. Public Health Nurs. 2006;23(1):27-36. [ PubMed : 16460418 ] 60. Cesario SK. Care of the Native American woman: strategies for practice, education, and research. J Obstet Gynecol Neonatal Nurs. 2001;30(1):13-19. [ PubMed : 11277158 ] 61. Fleischhacker S, Vu M, Ries A, McPhail A. Engaging tribal leaders in an American Indian healthy eating project through modified talking circles. Fam Community Health. 2011;34(3):202-210. [ PubMed : 21633212 ] 62. Ford CD, Kim MJ, Dancy BL. Perceptions of hypertension and contributing personal and environmental factors among rural Southern African American women. Ethn Dis. 2009;19(4):407-413. [ PMC free article : PMC2827201 ] [ PubMed : 20073141 ] 63. Johnson RE, Green BL, Anderson-Lewis C, Wynn TA. Community health advisors as research partners: an evaluation of the training and activities. Fam Community Health. 2005;28(1):41-50. [ PubMed : 15625505 ] 64. Strickland CJ, Squeoch MD, Chrisman NJ. Health promotion in cervical cancer prevention among the Yakama Indian women of the Wa'Shat Longhouse. J Transcult Nurs. 1999;10(3):190-196. [ PubMed : 10693405 ] 65. Struthers R, Hodge FS, De Cora L, Geishirt-Cantrell B. The experience of native peer facilitators in the campaign against type 2 diabetes. J Rural Health. 2003;19(2):174-180. [ PMC free article : PMC3102517 ] [ PubMed : 12696854 ] 66. Struthers R, Hodge FS, Geishirt-Cantrell B, De Cora L. Participant experiences of Talking Circles on type 2 diabetes in two Northern Plains American Indian Tribes. Qual Health Res. 2003;13(8):1094-1115. [ PMC free article : PMC3103143 ] [ PubMed : 14556421 ] 67. Wang C, Burris MA. Photovoice: concept, methodology, and use for participatory needs assessment. Health Educ Behav. 1997;24(3):369-387. [ PubMed : 9158980 ] 68. Dahan R, Dick R, Moll S, et al. Using photography to: help people share their ideas, improve our communities, give a voice to those not heard. 2007. Accessed March 20, 2018. https://www ​.naccho.org ​/uploads/downloadable-resources ​/Programs ​/Public-Health-Infrastructure ​/Photovoice-Manual.pdf 69. StoryCenter. Accessed March 21, 2018. https://www ​.storycenter.org/about/ 70. United States Census Bureau. QuickFacts—Santa Clara County, California. 2017. Accessed May 21, 2018. https://www ​.census.gov ​/quickfacts/fact/table ​/santaclaracountycalifornia ​/PST045217 71. Santa Clara County Public Health. Health and Social Inequity in Santa Clara County 2011. Accessed May 21, 2018. https://www.sccgov.org/sites/phd/hi/hd/Documents/shipexec-summary.pdf 72. Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the Household Food Security Scale. Am J Public Health. 1999;89(8):1231-1234. [ PMC free article : PMC1508674 ] [ PubMed : 10432912 ] 73. Berube A. City and metropolitan income inequality data reveal ups and downs through 2016. February 5, 2018. Accessed October 23, 2018. https://www ​.brookings ​.edu/research/city-and-metropolitan-income-inequality-data-reveal-ups-and-downs-through-2016/ 74. Grundy SM, Brewer HB Jr, Cleeman JI, et al. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscl Thromb Vasc Biol. 2004;24(2):e13-e18. doi:10.1161/01.ATV.0000111245.75752.C6 [ PubMed : 14766739 ] [ CrossRef ] 75. International Diabetes Federation. The IDF consensus worldwide definition of the metabolic syndrome. 2006. Accessed March 21, 2018. http://www ​.pitt.edu/∼super1 ​/Metabolic/IDF1.pdf 76. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall; 1986. 77. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38-48. [ PubMed : 10170434 ] 78. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51(3):390-395. [ PubMed : 6863699 ] 79. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society. J Am Coll Cardiol. 2014;63(25 Pt B):2985-3023. [ PubMed : 24239920 ] 80. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Int Med. 1998;158(16):1789-1795. [ PubMed : 9738608 ] 81. Yu L, Buysse DJ, Germain A, et al. Development of short forms from the PROMIS sleep disturbance and Sleep-Related Impairment item banks. Behav Sleep Med. 2011;10(1):6-24. [ PMC free article : PMC3261577 ] [ PubMed : 22250775 ] 82. National Heart, Lung, and Blood Institute. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. NIH Publication No. 00-4084. US Department of Health and Human Services, Public Health Service. Published October 1998. Accessed July 6, 2020. https://www ​.ncbi.nlm ​.nih.gov/books/NBK2003/ 83. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension. 2005;45(1):142-161. [ PubMed : 15611362 ] 84. PhenX (consensus measures of Phenotypes and eXposures). PhenX Toolkit Release Details, Ver 4.2. 2011. Accessed March 21, 2018. https://www ​.phenxtoolkit ​.org/about/releases/26 85. Weathers FW, Litz BT, Herman DS, Huska JA, Keane TM. The PTSD Checklist: reliability, validity, and diagnostic utility. Paper presented at: Annual Meeting of the International Society for Traumatic Stress Studies; 1993; San Antonio, TX. 86. Sawchuk CN, Russo JE, Bogart A, et al. Barriers and facilitators to walking and physical activity among American Indian elders. Prev Chronic Dis. 2011;8(3):A63. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC3103568/ [ PMC free article : PMC3103568 ] [ PubMed : 21477503 ] 87. Ferucci ED, Schumacher MC, Lanier AP, et al. Arthritis prevalence and associations in American Indian and Alaska Native people. Arthritis Rheum. 2008;59(8):1128-1136. [ PMC free article : PMC2925516 ] [ PubMed : 18668615 ] 88. Teufel-Shone NI, Jiang L, Beals J, et al. Demographic characteristics and food choices of participants in the Special Diabetes Program for American Indians Diabetes Prevention Demonstration Project. Ethn Health. 2015;20(4):327-340. [ PMC free article : PMC5108238 ] [ PubMed : 24954106 ] 89. Meyer AM, Evenson KR, Morimoto L, Siscovick D, White E. Test-retest reliability of the Women's Health Initiative physical activity questionnaire. Med Sci Sports Exerc. 2009;41(3):530-538. [ PMC free article : PMC2692735 ] [ PubMed : 19204598 ] 90. Ainsworth BE, Haskell WL, Herrmann SD, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575-1581. [ PubMed : 21681120 ] 91. Cole JC, Rabin AS, Smith TL, Kaufman AS. Development and validation of a Rasch-derived CES-D short form. Psychol Assess. 2004;16(4):360-372. [ PubMed : 15584795 ] 92. Haswell MR, Kavanagh D, Tsey K, et al. Psychometric validation of the Growth and Empowerment Measure (GEM) applied with Indigenous Australians. Aust N Z J Psychiatry. 2010;44(9):791-799. [ PubMed : 20815665 ] 93. Wing RR, Hamman RF, Bray GA, et al. Achieving weight and activity goals among diabetes prevention program lifestyle participants. Obes Res. 2004;12(9):1426-1434. [ PMC free article : PMC2505058 ] [ PubMed : 15483207 ] 94. Kraemer HC, Stice E, Kazdin A, Offord D, Kupfer D. How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. Am J Psychiatry. 2001;158(6):848-856. [ PubMed : 11384888 ] 95. Chmura Kraemer H, Kiernan M, Essex M, Kupfer DJ. How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychol. 2008;27(2S):S101-S108. [ PMC free article : PMC3376898 ] [ PubMed : 18377151 ] 96. Wallerstein NB, Duran B. Using community-based participatory research to address health disparities. Health Promot Pract. 2006;7(3):312-323. [ PubMed : 16760238 ] 97. Wingood GM, DiClemente RJ. The ADAPT-ITT model: a novel method of adapting evidence-based HIV interventions. J Acquir Immun Defic Syndr. 2008;47 Suppl 1:S40-S46. [ PubMed : 18301133 ] 98. Barrera M Jr., Castro FG, Strycker LA, Toobert DJ. Cultural adaptations of behavioral health interventions: a progress report. J Consult Clin Psychol. 2013;81(2):196-205. [ PMC free article : PMC3965302 ] [ PubMed : 22289132 ] 99. Brave Heart MYH. The return to the sacred path: healing the historical trauma and historical unresolved grief response among the Lakota through a psychoeducational group intervention. Smith Coll Stud Soc Work. 1998;68(3):287-305. 100. Brave Heart MYH. Oyate ptayela: rebuilding the Lakota Nation through addressing historical trauma among Lakota parents. J Hum Behav Soc Environment. 1999;2(1-2):109-126. 101. Brave Heart MYH, DeBruyn LM. The American Indian Holocaust: healing historical unresolved grief. Am Indian Alaska Native Men Health Res. 1998;8(2):56-78. [ PubMed : 9842066 ] 102. Aziz Z, Absetz P, Oldroyd J, Pronk NP, Oldenburg B. A systematic review of real-world diabetes prevention programs: learnings from the last 15 years. Implement Sci. 2015;10:172. doi:10.1186/s13012-015-0354-6 [ PMC free article : PMC4681022 ] [ PubMed : 26670418 ] [ CrossRef ] 103. Potthoff RF, Tudor GE, Pieper KS, Hasselblad V. Can one assess whether missing data are missing at random in medical studies? Stat Methods Med Res. 2006;15(3):213-234. [ PubMed : 16768297 ] 104. Wadden TA, West DS, Neiberg RH, et al. One-year weight losses in the Look AHEAD study: factors associated with success. Obesity (Silver Spring). 2009;17(4):713-722. [ PMC free article : PMC2690396 ] [ PubMed : 19180071 ] 105. Venditti EM, Bray GA, Carrion-Petersen ML, et al. First versus repeat treatment with a lifestyle intervention program: attendance and weight loss outcomes. Int J Obes (Lond). 2008;32(10):1537-1544. [ PMC free article : PMC2574826 ] [ PubMed : 18711387 ] 106. Chao D, Farmer DF, Sevick MA, et al. The value of session attendance in a weight-loss intervention. Am J Health Behav. 2000;24:413-421. Related publications 107. Rosas LG, Vasquez JJ, Naderi R, et al. Development and evaluation of an enhanced diabetes prevention program with psychosocial support for urban American Indians and Alaska Natives: a randomized controlled trial. Contemp Clin Trials. 2016;50:28-36. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC6691492/ [ PMC free article : PMC6691492 ] [ PubMed : 27381232 ] Acknowledgments We would like to thank all of the trial participants and their families for their contributions to this research. We would also like to thank all AICAB members and all of the data collection and intervention staff. Research reported in this report was funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#AD-1306-02172). Further information available at: https://www.pcori.org/research-results/2013/comparing-two-diabetes-prevention-programs-american-indian-and-alaska-native Appendices Appendix. Supplemental Table 1. The Effects of Potential Moderators (PDF, 84K) Institution Receiving the PCORI Award: Stanford University Original Project Title: A Patient-Centered Strategy for Improving Diabetes Prevention in Urban American Indians PCORI ID: AD-1306-02172 ClinicalTrials.gov ID: NCT02266576 Suggested citation: Goldman Rosas L, Vasquez JJ, Lv N, et al. (2020). Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/07.2020.AD.130602172 Disclaimer The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee. Copyright © 2020. Stanford University School of Medicine. All Rights Reserved. This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License which permits noncommercial use and distribution provided the original author(s) and source are credited. (See https://creativecommons.org/licenses/by-nc-nd/4.0/ Bookshelf ID: NBK620126 PMID: 41505575 DOI: 10.25302/07.2020.AD.130602172 Share Views PubReader Print View Cite this Page Goldman Rosas L, Vasquez JJ, Lv N, et al. Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community [Internet]. Washington (DC): Patient-Centered Outcomes Research Institute (PCORI); 2020 Jul. doi: 10.25302/07.2020.AD.130602172 PDF version of this title (1.4M) In this Page Background Participation of Patients and Other Stakeholders Methods Results Discussion Conclusions References Related publications Acknowledgments Appendices Other titles in this collection PCORI Final Research Reports Related information NLM Catalog Related NLM Catalog Entries PMC PubMed Central citations PubMed Links to PubMed Recent Activity Clear Turn Off Turn On Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native... Comparing Two Diabetes Prevention Programs for American Indian and Alaska Native Adults in an Urban Community Your browsing activity is empty. Activity recording is turned off. Turn recording back on See more... Follow NCBI Twitter Facebook LinkedIn GitHub NCBI Insights Blog Connect with NLM Twitter Facebook Youtube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov