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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Curr Obes Rep. 2021 Jul 14;10(3):332–341. doi: 10.1007/s13679-021-00445-x

Opportunities to Address Obesity Disparities Among High-Risk Latino Children and Adolescents

Erica G Soltero 1, Teresia M O’Connor 1, Deborah Thompson 1, Gabriel Q Shaibi 2
PMCID: PMC9116051  NIHMSID: NIHMS1789958  PMID: 34263434

Abstract

Purpose of Review

This review highlights obesity-related disparities among Latino children and adolescents, discusses the social determinants of health (SDoH) that drive disparities, and presents case studies of strategies for reducing disparities and promoting health equity.

Recent Findings

Recommended strategies for reducing obesity-related disparities include the use of culturally grounded programming, multi-sector collaborations, and technology. We present two exemplar studies that demonstrate that integrating cultural values and enhancing the overall cultural fit of prevention programs can increase engagement among high-risk Latino families. We also examine the use of multi-sector collaborations to build community capacity and address key SDoH that impact health behaviors and outcomes. Our last example study demonstrates the utility of technology for engaging youth and extending the reach of prevention strategies in vulnerable communities.

Summary

To address growing obesity-related disparities, there is an urgent need to develop and test these strategies among high-risk, vulnerable populations like Latino children and adolescents.

Keywords: Obesity, Children, Adolescents, Social determinants of health, Prevention interventions

Introduction

The prevalence of pediatric obesity in the USA continues to increase in all age groups, particularly among Latino children and adolescents who exhibit higher levels of obesity (25.8% vs 18.5% general population) and severe obesity (12.4% vs 7.9% general population) [1, 2]. While obesity-related disparities affect many racial and ethnic subgroups, Latino youth represent about 23% of the pediatric population and are the fastest growing pediatric subgroup in the USA [3]. Therefore, this review will focus specifically on the Latino population as this group remains underrepresented in research and underserved in many communities. It is estimated that 55% of children and 80% of adolescents with obesity will continue to have obesity as they transition into adulthood [4]. This significantly increases their risk for obesity-related chronic diseases as well as lifetime medical costs, which are 42% higher for those with obesity compared to those without obesity [5]. Eliminating racial and ethnic minority health disparities could reduce direct medical costs by an estimated $230 billion dollars [6]. Unfortunately, the literature on obesity prevention strategies and interventions for high-risk Latino children and adolescents remains limited, and disparities in obesity in this rapidly growing population continue to widen [79]. This article will provide a brief overview of obesity-related disparities among Latino children and adolescents, discuss contributing social and contextual factors, and present case studies of promising strategies for reducing disparities and promoting health equity in this key demographic.

Disparities in Obesity-Related Diseases

The presence of childhood obesity is associated with more than 29 developmental, physical, and psychosocial health complications [10]. In regard to physical consequences, Latino youth are disproportionately burdened by the metabolic syndrome (16.4% Mexican American youth vs 5.4% in non-Latino White youth), which represents a clustering of obesity and related risk factors (e.g., hypertension, dyslipidemia, and impaired glucose metabolism) [11]. These risk factors prospectively predict the development of type 2 diabetes and cardiovascular diseases (CVD) [1113]. Indeed, Latino youth with obesity exhibit early signs of these disease processes as indicated by high rates of prediabetes and subclinical atherosclerosis [14, 15]. More proximal than future risk of T2D and CVD among Latino youth is non-alcoholic fatty liver disease, which impacts nearly 50% of Latino youth with obesity [16, 17]. Although the physical health consequences of obesity are alarming from a clinical perspective, it is the psychosocial and emotional consequences that may be most important and proximally perceived to children and adolescents. Although not specific to Latinos, children and adolescents with obesity experience weight-based stigma and discrimination, which can lead to feelings of depression, social isolation, and body image dissatisfaction [10, 18]. Several studies have also established that Latino adolescents with obesity report decreased health-related quality of life compared to youth without obesity, which can have long-term consequences for psychosocial health [16, 19]. Impairments to psychosocial health such as poor quality of life can increase cardiometabolic disease risk factors further contributing to the complications associated with obesity [20].

Drivers of Obesity Disparities

In addition to the complex interplay between individual biological and behavioral factors associated with the development of pediatric obesity, it is clear that broader social and contextual influences contribute to obesity and related chronic diseases [2124]. Furthermore, obesity-related health disparities are rooted in structural inequalities defined as the personal, interpersonal, institutional, and systemic drivers that “structure” differential or unequal access to health promotion and disease prevention opportunities [2527]. This may include policies, healthcare, race, and other domains in which systematic disadvantages exist for one group of individuals compared to another group of individuals [27]. These inequalities or structural differences give rise to root causes or social determinants of health (SDoH), which are displayed in the inner circle of the conceptual model presented in Figure 1 [25, 26]. SDoH are defined as the conditions in which people live, worship, work, and go to school that ultimately shape health outcomes [27]. For example, structural factors such as restrictive immigration policies contribute to SDoH such as limited employment opportunities, limited access to public services, and fear and stress regarding immigration law enforcement [27]. Latino immigrant workers perform a disproportionate amount of unskilled, high-risk jobs that typically do not provide benefits including health insurance [28, 29]. Nearly two-thirds of undocumented Latino immigrants lack health insurance, and many are not eligible for public insurance programs [27, 30]. Furthermore, anti-immigration social and political rhetoric as well as immigration law enforcement is also linked to reduced participation in other public and social welfare programs among Latino families [27, 30]. Lack of access to assistance programs can have consequences such as food insecurity, which is associated with obesity in Hispanic youth [31, 32]. A study among immigrant Mexican American households reported a 10% increase in food insecurity after the passage of immigration-related legislation [33]. Although upstream of traditional determinants of obesity (e.g., individual behaviors), structural inequalities such as immigration policy contribute to SDoH that are interdependent and can have a combined effect on downstream weight-related outcomes.

Figure 1.

Figure 1

Conceptual model of community-driven solutions to address structural inequities and social determinants of health. Source: National Academies of Science, Communities in Action: Pathways to Health Equity

Addressing obesity disparities requires strategies and interventions to consider the SDoH that disproportionately burden minority adolescents [34, 35]. Unfortunately, current strategies for addressing SDoH are not well defined, and existing frameworks focus on the identification of upstream SDoH and how they are linked with downstream health outcomes [34]. For example, the Increasing Equity Impact in Obesity Prevention framework put forth by Kumanyika provides important approaches to increasing health equity in upstream policy, systems, and environmental change strategies to directly address or account for SDoH [25, 3638]. Such “root changes” to policies and systems are desperately needed, and efforts should be ongoing. However, they can be politically challenging and costly and may take generations to improve health outcomes [21]. Developing interventions that carefully consider SDoH in their design while integrating plans for sustainability and scaling may show more immediate promise in reducing obesity-related disparities in Latino children and adolescents [34, 39].

Opportunities to Reduce Obesity Disparities

Culturally Grounded Programming

The US Preventive Services Task Force has identified that the most successful obesity prevention programs for addressing disparities among minority youth are those that are culturally grounded in the beliefs, values, and circumstances specific to the group of interest [40, 41]. Culture is a key driver of health beliefs, practices, and behaviors that is associated with obesity and related disease risk in Latino youth [4244]. Because culturally grounded interventions reflect the characteristics of the specific culture, they are recognized as critical for increasing the reach, engagement, and effectiveness of disease prevention strategies among minority populations [43, 45]. Unfortunately, few culturally grounded obesity and related chronic disease prevention interventions have been developed and tested among high-risk Latino children and adolescents [43, 44, 46].

Barrera et al. identified several prominent approaches for developing culturally grounded disease prevention interventions [47]. The first approach involves cross-collaboration and knowledge exchange between researchers and community stakeholders to ground the intervention. Investigators provide knowledge on theory, behavior change techniques, and add rigor in evaluation, while community stakeholders provide critical knowledge on important elements of the culture and local context that can be leveraged to enhance uptake and efficacy [47]. As a case study for this approach, we present Every Little Step Counts (ELSC), a culturally grounded diabetes prevention program for Latino adolescents with obesity [4851]. Developed by a Latino-serving, safety-net clinic in Phoenix, AZ, in response to the growing number of Latino children and adolescents being referred for diabetes screening, ELSC was grounded in the needs, resources, and context of the local community as a way to increase access to health promotion and diabetes prevention programming for an underserved population. Trust, respect, and personal interactions were prioritized to enhance rapport with the community, and bilingual/bicultural health educators were trained to deliver the curriculum [52]. Researchers were invited to collaborate, with the goal of adding a rigorous evaluation component to the program and further explicating key evidenced-based behavioral skills training and strategies within a unified conceptual model [53]. While cultural fit was already a consideration, the team worked collaboratively to operationalize components from evidenced-based diabetes prevention programs into the cultural context of Latino families. For example, the partnership identified the need to emphasize cultural strengths that focused on family unit. “Familismo or familism” and “collectivism” are central and important cultural values that refer to the importance of the family unit and influence identity, involvement, and decision-making [53, 54]. Thus, these cultural values became leverage points for promoting behavior change and were integrated into the intervention. Program content and materials were delivered in a family-centered manner focusing on family roles and responsibilities that support behavior change in youth [48, 53]. Activities were also family focused such as youth and parents planning and preparing a healthy family meal together [48]. Youth set behavioral goals, and families were encouraged to discuss challenges to goal attainment while sharing strategies for overcoming challenges [48]. The intervention led to significant improvements in obesity-related outcomes as well as self-reported quality of life [55]. Furthermore, family social support was a key mediator in the intervention [56].

Another approach to developing culturally grounded interventions includes cultural adaptations of existing evidence-based interventions. In this approach, the core elements of an evidence-based intervention are retained, while cultural values and contextual factors are integrated into the intervention material and implementation strategies [45, 47]. This approach is exemplified in the Papas Saludables, Niños Saludables (PSNS) study, a 9-week family-based lifestyle intervention for Latino fathers and children (5–11 years) [57, 58]. PSNS was adapted from the Australian Healthy Dads, Healthy Kids program, which has demonstrated clinically meaningful weight loss among fathers and significant improvements in physical activity and dietary intake among children [59]. Given the high prevalence of obesity among Latino men, a cultural adaptation of this effective, evidence-based program had high potential for reaching Latino fathers and their children to reduce obesity-related health disparities [58, 60]. The process of culturally adapting Healthy Dads, Health Kids was guided by the Ecological Validity Model proposed by Bernal and Sáez-Santiago, which recommends adaptations across eight dimensions: program goals, concepts, methods, content, persons, metaphors, language, and context [45]. Program adaptations were conducted iteratively in collaboration with expert panels, which included a professional panel consisting of researchers with expertise in health promotion and parenting among Latino fathers and families, and a family panel, which consisted of Latino mothers, fathers, and their children. Formative qualitative work conducted with Latino families revealed that “familism” and “collectivism” were important cultural values; however, more nuanced “deep structure” subthemes emerged such as perceptions of gender roles in caretaking and providing for one’s family [58]. For example, Latino fathers believed their main role was to provide and be the “breadwinner” for their family. Providing for one’s family is a strong Latino cultural value; however, Latino men are more likely to work physically demanding jobs with nontraditional working hours that may limit opportunities for engaging with their children around health behaviors [61]. Participants also reported that beliefs in traditional gender roles may differentiate the way that fathers interact with sons as compared to daughters [58]. To address these important cultural values, the intervention was adapted to include group discussions about the importance of parenting among fathers and strategies for modeling and supporting health behaviors for both sons and daughters. The program was implemented on the weekend at times when fathers were able to attend, and implementers emphasized that leisure physical activity could reduce work-related stress from physically demanding jobs. Quality time with one’s children was emphasized as opposed to quantity of time. To further build cohesion, participating fathers were encouraged to recognize that there are other Latino fathers like them who want to build close relationships with their children [57]. In feasibility testing, PSNS demonstrated high attendance (72%) and exceptional satisfaction (100%) among families [57]. The intervention will be further refined and tested in a family randomized group-treatment trial. This example highlights how integrating cultural values and contextual factors into existing evidence-based intervention strategies can increase engagement, even among traditionally underrepresented populations like Latino fathers [61].

In addition to demonstrating effectiveness among school-aged children and adolescents, culturally grounded interventions have also been used to successfully engage younger Latino pre-school-aged children and families [62, 63]. In all of these examples, community engagement is an overlapping theme critical for developing culturally grounded disease prevention programs [21, 39]. These approaches can increase understanding and respect for cultural practices and contextual factors that contribute to health disparities as well as the ones that promote resilience and serve as potential leverage points for amplifying health behaviors and preventing disease [47]. An important next step in the progress of culturally grounded prevention interventions is to identify how cultural components and adaptations contribute to intervention outcomes [47]. This information can shed light on the underlying mechanisms by which culturally grounded interventions “work” to improve behaviors and outcomes. Identifying intervention mechanisms or critical ingredients can also be used to expand or adapt existing behavioral theories to increase the fidelity and fit within cultural subgroups [64].

Multi-sector Collaborations

The breadth and complexity of SDoH require multi-sector partnerships that extend beyond the healthcare system to include multiple stakeholders across sectors (e.g., health, housing, transportation, environment, and economy) [22, 35, 65]. The primary goal of clinic-community partnerships is to optimize the strengths, resources, and knowledge within the partners to address a health and research priority within the community [66, 67]. In doing so, this approach can lead to the development of comprehensive, multi-level interventions that are accessible, sustainable, and tailored to meet the needs of the population of interest while considering SDoH [26, 68, 69]. Pairing clinical efforts with community resources is a recognized strategy for considering SDoH and reducing obesity disparities, yet few clinic-community partnership focused on obesity are described in the literature [21, 70].

Building upon the success of ELSC described above, the intervention was adapted for Latino families with younger children (8–12 years) and anchored in a predominantly Latino community. The partnership was extended to connect a Federally Qualified Health Center (FQHC) with a nearby YMCA through support from a State Health Department [55]. Providers and staff from the FQHC were engaged through monthly provider meetings, a research coordinator was embedded within the clinic, and the electronic medical record (EMR) was modified to facilitate recruitment and foster communication with providers [55]. Participants (children and parents) completed a clinic visit with their provider before and after the intervention, and a mid-intervention report was uploaded to the EMR to update clinicians of their progress toward achieving their behavioral health goals and study outcomes (e.g., HbA1c, BMI). Partnering with an FQHC in a low-income community reduced barriers to healthcare access for the many adult participants who were uninsured. As was often the case, participating youth were established patients; however, 60% of parents did not have a regular healthcare provider. In response, we developed a system for establishing all participating parents as patients at the FQHC, providing a pathway for them to see a primary care provider prior to enrollment in the program [55]. The YMCA served as the delivery site for the 12-week program. The mission and vision of the Valley of the Sun YMCA is to build strong kids, strengthen families, and bring communities together. They are committed to creating an inclusive environment for everyone by providing financial aid and assistance for memberships [71]. Connecting to the YMCA provided families with a safe physical activity resource that was accessible within their community. To further embed the ELSC program within the YMCA’s existing infrastructure and services, they began providing family-based exercise classes and provided classroom space for health educators to deliver nutrition education [55]. As families completed the program, we identified “Champion Families” or families with high levels of attendance and engagement. These Champion Families were invited to return to the program and assist the research team in leading and implementing the program among a new cohort of families. This allowed us to build capacity and empower families with the knowledge and skills to assist other families in their community. This study resulted in significant decreases in body fat percentage and significant improvements in quality of life among children and adults as well as significant reductions in HbA1c among adults [55]. The promising findings from the development and testing of the ELSC intervention support the use of multi-sector partnerships to develop multi-level, lifestyle interventions that are culturally congruent, build community capacity, consider SDoH, and represent more efficacious models of disease prevention that address disparities in high-risk populations [55].

Without multi-sector collaborations that consider social and environmental factors that impact health behaviors and disease outcomes, current attempts to reduce obesity disparities will be limited [21]. In these partnerships, the community’s capacity to deliver prevention services is strengthened, while clinical efforts and preventive services can be extended into vulnerable communities [21]. In doing so, these partnerships facilitate the translation of prevention efforts into “real-world” settings expediting the translation of research to high-risk, health-disparate populations with the greatest need [72, 73]. To advance the use and implementation of clinic-community partnerships, there is a greater need for increased models and frameworks to guide the development, evaluation, and reporting on these types of partnerships [74]. Ultimately, a reimbursement model or payment system will be necessary to facilitate scaling and sustainability of such promising collaborations.

Technology-Based Strategies

Technology-based interventions include the delivery of behavior change and disease prevention interventions using digital devices with Internet or wireless remote capabilities and are recommended as a strategy for overcoming SDoH that limit participation in in-person interventions [28, 65, 75, 76]. For some high-risk youth and families, these SDoH include lack of transportation, nontraditional parent work schedules, and additional needs like childcare for younger siblings [35]. However, technology allows for interventions to be remotely delivered directly to adolescents in their home environment reducing the need for transportation and allowing for more flexible scheduling for working parents [7779]. Additional strategies used to target SDoH like food insecurity and limited access to physical activity resources include the use of personalized notifications [79]. For example, interventions that utilize Geographic Information Systems Technology can notify participants of affordable food resources and safe physical activity resources in their neighborhood environment [79]. Personalized text messages are another tool that has been used to foster social support and cohesion between participants and implementers regardless of geographical distance [78]. Many of these strategies can be cost-effective because they leverage devices like smartphones that may already be owned by the participant [80]. While the digital divide persists across racial and ethnic subgroups in regard to laptop ownership and access to high-speed Internet, Latino and non-Hispanic White youth report the same level of smartphone ownership (71%) [81]. Today’s adolescents were exposed to technology at an earlier age and are the highest users of digital devices and text-message communication, and 94% access the Internet on a daily basis [82, 83]. Thus, in addition to being a potential strategy for overcoming SDoH, technology-based strategies and interventions are age-appropriate for delivering obesity and disease prevention interventions [84, 85]. Unfortunately, minority youth in vulnerable communities remain underrepresented in technology-based studies [34].

To develop technology-based strategies that are considerate of SDoH, the population of interest should be involved in every phase of the development and design process to ensure that the intervention meets the needs and fits the context of the population [79, 85]. Capturing the behavioral and technology use patterns within the youth’s social, built, and political environments provides an avenue for increasing the engagement, satisfaction, and cultural appropriateness of the intervention [85, 86]. However, few studies have engaged youth in the design and development of technology-based interventions [82, 87, 88]. The “TXT Me!” study developed by Thompson et al. is an exemplar study that adds support for the inclusion of underrepresented minority adolescents in the development process [89]. This study was a 12-week intervention that used a personal activity tracker and text messages grounded in the self-determination theory to increase physical activity among an ethnically diverse (27% Latino/36% Black) population of adolescents (14–17 years). Participants completed a web-based survey focused on topics such as engagement with family around activity, perceptions of using an activity tracker, the frequency and type of text messages desired, and their motivation and personal values associated with physical activity. This survey was followed by an in-depth interview to clarify survey responses and ensure that results were interpreted as intended [89]. This information was used to guide the implementation of the text message intervention. For example, youth felt it would be ideal to receive messages before school between 6am and 8am. Youth responded to questions about their cell phone access, discussed their family data plans, and shared information on family rules about texting such as no use of profanity. Youth expressed that text messages should be short (<160 characters) and include emoticons and exclamation points. Participants found that messages highlighting the values of “being responsible,” “being healthy,” and “being successful” promoted a sense of connectedness, a central component of the self-determination theory. This formative research resulted in the development of a library of over 70 text messages that were acceptable among teens and aligned with family social environment around physical activity and family rules regarding text messaging. These text messages were piloted in a 12-week feasibility study. Youth found the text messages engaging (85%), motivational (55%), and increased daily physical activity [90]. This study demonstrates that partnering with youth in the development process can lead to an engaging, cost-effective intervention that leverages familiar and convenient technology to promote physical activity [90, 91].

Technology-based strategies and interventions have high potential to change the way health promotion programs are delivered and can extend the reach of disease prevention strategies in vulnerable communities impacted by SDoH [34]. Interventions, including the example presented above, have demonstrated that text messaging and social media have shown promise for improving diet and physical activity behaviors among youth [92, 93]. Additionally, several studies have developed telehealth or web-based obesity prevention programs and found this approach to be feasible and acceptable among adolescents [9497]. Even video game systems which are typically thought of as drivers of sedentary behaviors can be used to improve health behaviors through health-focused video games and exergames [91, 98, 99]. However, this area of research is still evolving, and findings have been inconclusive [100]. There is a clear need to develop and test technology-based obesity prevention strategies among high-risk, minority youth and to develop and implement these strategies in a manner that considers the SDoH while engaging the family unit rather than a single individual [22, 35]. Strategies to consider SDoH from the exemplar studies presented have been summarized and presented in Table 1.

Table 1.

Strategies to consider SDoH in health promotion and disease prevention from example interventions among Latino youth.

SDoH Strategy
Culture Bilingual/bicultural health educators conduct screening, data collection, and implement intervention content and materials in Spanish/English
Culturally grounded intervention content and curriculum that integrates Latino cultural values, norms, and traditions
Access to prevention services Collaborate with community-based clinics in geographic areas with high Hispanic populations to implement programs at no cost to uninsured or publicly insured youth and families
Collaborate with stakeholders to build or leverage existing community resources liked the YMCA to deliver programs in high-risk communities
Foster patient-provider relationships through clinic visits before and after the intervention and through progress reports
Access to healthcare Develop systems to link families with primary care settings such as processes for establishing care or accessing health promotion and disease prevention opportunities
Social cohesion/family support Deliver content in a family-focused manner with family-focused activities
Foster social support within and between families. For example, encourage youth to work with parents on setting behavior goals and ask families to share barriers to behavior goals with the group
Use technology such as messaging apps (i.e., WhatsApp) or social media to build social networks and connect participants to each other and to implementers
Health literacy Deliver health education and skills building in a culturally appropriate manner that acknowledges language and cultural values
Foster hands-on activities and active learning around health behaviors and outcomes. For example, ask families to plan and cook a healthy meal together
Use technology platforms such as messaging apps or social media to increase access to health promotion and materials
Transportation Provide transportation assistance to data collection appointments and intervention sessions in the form of bus fare or ride share vouchers
Develop resources near accessible public transportation options such as trains or bus stops
Use technology like videoconferencing platforms to extend the reach of programming regardless of geographical spaces
Access to physical activity resource Increase access to safe physical activity resources by delivering programs at safe, trusted, resources within the community such as community centers
Use technologies like Geographical Information Systems to identify safe, physical activity resources in the participant’s neighborhoods

Conclusion

Improving health equity is a health priority, and meeting this objective requires that we reduce obesity and disease disparities in high-risk populations [26]. Central to reducing obesity disparities in high-risk populations like Latino children and adolescents is the development of prevention interventions and strategies that consider the SDoH that exacerbate disparities, limit health behaviors, and contribute to disease outcomes [22]. We have presented three distinct avenues for doing this, including the use of culturally grounded programming, multi-sector partnerships, and technology-based approaches. In the exemplar studies presented, we demonstrated how these strategies can be used to successfully engage Latino families in effective prevention efforts. Community collaboration is a common thread that links the examples presented and could be considered essential for moving the field. Investigators should rigorously evaluate the impact of interventions and strategies that carefully consider SDoH on health behaviors and outcomes to advance our understanding of effective strategies for reducing obesity disparities in high-risk groups. As evidence builds, it may be leveraged to inform policies and programs that can address the SDoH that underpin disparities in order to achieve health equity at the population level.

Funding

The work presented in this review was supported by grants from the National Institutes of Health including grants from the National Institute of Diabetes and Digestive and Kidney Disease (R01DK10757901) and the National Institute on Minority Health and Health Disparities (U54MD002316) awarded to Dr. Shaibi; the National Heart, Lung, and Blood Institute (R34HL131726) awarded to Dr. O’Connor; and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD066305) awarded to Dr Thompson. This work is a publication of the United States Department of Agriculture/Agricultural Research Service (USDA/ARS) Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, and Drs. Soltero, O’Connor, and Thompson are supported by a USDA/ARS cooperative agreement #58-3092-0-001.

Footnotes

This article is part of the Topical Collection on Childhood Obesity

References

  • 1.Ogden CL, Fryar CD, Martin CB, Freedman DS, Carroll MD, Gu Q, et al. Trends in obesity prevalence by race and Hispanic origin-1999–2000 to 2017–2018. JAMA 2020;324(12):1208–10. 10.1001/jama.2020.14590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics 2018;141(3). 10.1542/peds.2017-3459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Ennis SR, Rios-Vargas M, Albert NG. Census briefs: the Hispanic population 2010 (2010 Census Briefs) D.C.: Washington; 2011. [Google Scholar]
  • 4.Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev 2016;17(2):95–107. 10.1111/obr.12334. [DOI] [PubMed] [Google Scholar]
  • 5.Bhupathiraju SN, Hu FB. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res 2016;118(11): 1723–35. 10.1161/CIRCRESAHA.115.306825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.LaVeist TA, Gaskin D, Richard P. Estimating the economic burden of racial health inequalities in the United States. Int J Health Serv 2011;41(2):231–8. 10.2190/HS.41.2.c. [DOI] [PubMed] [Google Scholar]
  • 7.Branscum P, Sharma M. A systematic analysis of childhood obesity prevention interventions targeting Hispanic children: lessons learned from the previous decade. Obes Rev 2011;12(5):e151–8. 10.1111/j.1467-789X.2010.00809.x. [DOI] [PubMed] [Google Scholar]
  • 8.Ismaeel A, Weems S, McClendon M, Morales FE. Interventions aimed at decreasing obesity in Hispanic children in the first 1000 days: a systematic review. J Immigr Minor Health 2018;20(5): 1288–93. 10.1007/s10903-017-0672-7. [DOI] [PubMed] [Google Scholar]
  • 9.Tamayo MC, Dobbs PD, Pincu Y. Family-centered interventions for treatment and prevention of childhood obesity in Hispanic families: a systematic review. J Community Health 2020;46: 635–43. 10.1007/s10900-020-00897-7. [DOI] [PubMed] [Google Scholar]
  • 10.Steinbeck KS, Lister NB, Gow ML, Baur LA. Treatment of adolescent obesity. Nat Rev Endocrinol 2018;14(6):331–44. 10.1038/s41574-018-0002-8. [DOI] [PubMed] [Google Scholar]
  • 11.Miller JM, Kaylor MB, Johannsson M, Bay C, Churilla JR. Prevalence of metabolic syndrome and individual criterion in US adolescents: 2001–2010 National Health and Nutrition Examination Survey. Metab Syndr Relat Disord 2014;12(10): 527–32. 10.1089/met.2014.0055. [DOI] [PubMed] [Google Scholar]
  • 12.Lorenzo C, Okoloise M, Williams K, Stern MP, Haffner SM. San Antonio Heart S. The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study. Diabetes Care 2003;26(11):3153–9. 10.2337/diacare.26.11.3153. [DOI] [PubMed] [Google Scholar]
  • 13.Christian Flemming GM, Bussler S, Korner A, Kiess W. Definition and early diagnosis of metabolic syndrome in children. J Pediatr Endocrinol Metab 2020;33(7):821–33. 10.1515/jpem-2019-0552. [DOI] [PubMed] [Google Scholar]
  • 14.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):1021–9. 10.1001/jama.2015.10029. [DOI] [PubMed] [Google Scholar]
  • 15.Caballero AE, Bousquet-Santos K, Robles-Osorio L, Montagnani V, Soodini G, Porramatikul S, et al. Overweight Latino children and adolescents have marked endothelial dysfunction and subclinical vascular inflammation in association with excess body fat and insulin resistance. Diabetes Care 2008;31(3):576–82. 10.2337/dc07-1540. [DOI] [PubMed] [Google Scholar]
  • 16.Schwimmer JB, Deutsch R, Kahen T, Lavine JE, Stanley C, Behling C. Prevalence of fatty liver in children and adolescents. Pediatrics 2006;118(4):1388–93. 10.1542/peds.2006-1212. [DOI] [PubMed] [Google Scholar]
  • 17.Welsh JA, Karpen S, Vos MB. Increasing prevalence of nonalcoholic fatty liver disease among United States adolescents, 1988–1994 to 2007–2010. J Pediatr 2013;162(3):496–500 e1. 10.1016/j.jpeds.2012.08.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Pont SJ, Puhl R, Cook SR, Slusser W, Section On O, Obesity S. Stigma experienced by children and adolescents with obesity. Pediatrics 2017;140(6). 10.1542/peds.2017-3034. [DOI] [PubMed] [Google Scholar]
  • 19.Brito E, Patrick DL, Konopken YP, Keller CS, Barroso CS, Shaibi GQ. Effects of a diabetes prevention programme on weight-specific quality of life in Latino youth. Pediatr Obes 2014;9(5): e108–11. 10.1111/ijpo.240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Nadeau K, Kolotkin RL, Boex R, Witten T, McFann KK, Zeitler P, et al. Health-related quality of life in adolescents with comorbidities related to obesity. J Adolesc Health 2011;49(1):90–2. 10.1016/j.jadohealth.2010.10.005. [DOI] [PubMed] [Google Scholar]
  • 21.Skelton JA, Palakshappa D, Moore JB, Irby MB, Montez K, Rhodes SD. Community engagement and pediatric obesity: incorporating social determinants of health into treatment. J Clin Transl Sci 2019;4(4):279–85. 10.1017/cts.2019.447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Haire-Joshu D, Hill-Briggs F. The next generation of diabetes translation: a path to health equity. Annu Rev Public Health 2019;40:391–410. 10.1146/annurev-publhealth-040218-044158. [DOI] [PubMed] [Google Scholar]
  • 23.Gowda S, Seibert T, Uli N, Farrell R. Pediatric obesity: endocrinologic and genetic etiologies and management. Current Cardiovascular Risk Reports 2019;13(12):39. 10.1007/s12170-019-0632-y. [DOI] [Google Scholar]
  • 24.Aggarwal B, Jain V. Obesity in children: definition, etiology and approach. Indian J Pediatr 2018;85(6):463–71. 10.1007/s12098-017-2531-x. [DOI] [PubMed] [Google Scholar]
  • 25.Kumanyika SK. A framework for increasing equity impact in obesity prevention. Am J Public Health 2019;109(10):1350–7. 10.2105/AJPH.2019.305221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Baciu A, Negussie Y, Geller A, Weinstein JN Communities in action: pathways to health equity Washington (DC) 2017. [PubMed] [Google Scholar]
  • 27.Philbin MM, Flake M, Hatzenbuehler ML, Hirsch JS. State-level immigration and immigrant-focused policies as drivers of Latino health disparities in the United States. Soc Sci Med 2018;199:29–38. 10.1016/j.socscimed.2017.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Brown T, Moore TH, Hooper L, Gao Y, Zayegh A, Ijaz S, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev 2019;7:CD001871. 10.1002/14651858.CD001871.pub4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Flippen CA. Laboring underground: the employment patterns of Hispanic immigrant men in Durham. NC Soc Probl 2012;59(1): 21–42. 10.1525/sp.2012.59.1.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Venkataramani M, Pollack CE, DeCamp LR, Leifheit KM, Berger ZD, Venkataramani AS. Association of maternal eligibility for the deferred action for childhood arrivals program with citizen children’s participation in the women, infants, and children program. JAMA Pediatr 2018;172(7):699–701. 10.1001/jamapediatrics.2018.0775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Papas MA, Trabulsi JC, Dahl A, Dominick G. Food insecurity increases the odds of obesity among young Hispanic children. J Immigr Minor Health 2016;18(5):1046–52. 10.1007/s10903-015-0275-0. [DOI] [PubMed] [Google Scholar]
  • 32.Florez KR, Katic BJ, Lopez-Cevallos DF, Murillo R, Cancel-Tirado D, Aponte-Soto L, et al. The double burden of food insecurity and obesity among Latino youth: understanding the role of generational status. Pediatr Obes 2019;14(9):e12525. 10.1111/ijpo.12525. [DOI] [PubMed] [Google Scholar]
  • 33.Potochnick S, Chen JH, Perreira K. Local-level immigration enforcement and food insecurity risk among Hispanic immigrant families with children: national-level evidence. J Immigr Minor Health 2017;19(5):1042–9. 10.1007/s10903-016-0464-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Thornton PL, Kumanyika SK, Gregg EW, Araneta MR, Baskin ML, Chin MH, et al. New research directions on disparities in obesity and type 2 diabetes. Ann N Y Acad Sci 2020;1461(1): 5–24. 10.1111/nyas.14270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Braveman PA, Kumanyika S, Fielding J, Laveist T, Borrell LN, Manderscheid R, et al. Health disparities and health equity: the issue is justice. Am J Public Health 2011;101(Suppl 1):S149–55. 10.2105/AJPH.2010.300062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chin MH, Clarke AR, Nocon RS, Casey AA, Goddu AP, Keesecker NM, et al. A roadmap and best practices for organizations to reduce racial and ethnic disparities in health care. J Gen Intern Med 2012;27(8):992–1000. 10.1007/s11606-012-2082-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kumanyika S Getting to equity in obesity prevention: a new framework Washington D.C Nam Perspectives Discussion Paper, National Academy of Medicine; 2017. [Google Scholar]
  • 38.Palmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social Determinants of health: future directions for health disparities research. Am J Public Health 2019;109(S1):S70–S1. 10.2105/AJPH.2019.304964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Taveras EM, Marshall R, Sharifi M, Avalon E, Fiechtner L, Horan C, et al. Connect for Health: design of a clinical-community childhood obesity intervention testing best practices of positive outliers. Contemp Clin Trials 2015;45(Pt B):287–95. 10.1016/j.cct.2015.09.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Whitlock EP, O’ Connor EA, Williams SB, Beil TL, Lutz KW. Effectiveness of weight management interventions in children: a targeted systematic review for the USPSTF. Pediatrics 2010;125(2):e396–418. 10.1542/peds.2009-1955. [DOI] [PubMed] [Google Scholar]
  • 41.Seo DC, Sa J. A meta-analysis of obesity interventions among U.S. minority children. J Adolesc Health 2010;46(4):309–23. 10.1016/j.jadohealth.2009.11.202. [DOI] [PubMed] [Google Scholar]
  • 42.Hasson RE, Adam TC, Pearson J, Davis JN, Spruijt-Metz D, Goran MI. Sociocultural and socioeconomic influences on type 2 diabetes risk in overweight/obese African-American and Latino-American children and adolescents. J Obes 2013;2013:512914–9. 10.1155/2013/512914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Cruz P, Granados A. Type 2 diabetes in Latino youth: a clinical update and current challenges. Curr Probl Pediatr Adolesc Health Care 2019;49(1):16–22. 10.1016/j.cppeds.2018.11.008. [DOI] [PubMed] [Google Scholar]
  • 44.McCurley JL, Crawford MA, Gallo LC. Prevention of type 2 diabetes in U.S. Hispanic youth: a systematic review of lifestyle interventions. Am J Prev Med 2017;53(4):519–32. 10.1016/j.amepre.2017.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Bernal G, Domenech Rodriguez MM. Advances in Latino family research: cultural adaptations of evidence-based interventions. Fam Process 2009;48(2):169–78. 10.1111/j.1545-5300.2009.01275.x. [DOI] [PubMed] [Google Scholar]
  • 46.Leung MM, Cavalcanti OB, El Dada A, Brown M, Mateo KF, Yeh MC. Treating obesity in Latino children: a systematic review of current interventions. Int J Child Health Nutri 2017;6(1):1–15. [Google Scholar]
  • 47.Barrera M Jr, Castro FG, Steiker LK. A critical analysis of approaches to the development of preventive interventions for subcultural groups. Am J Community Psychol 2011;48(3–4): 439–54. 10.1007/s10464-010-9422-x. [DOI] [PubMed] [Google Scholar]
  • 48.Williams AN, Konopken YP, Keller CS, Castro FG, Arcoleo KJ, Barraza E, et al. Culturally-grounded diabetes prevention program for obese Latino youth: Rationale, design, and methods. Contemp Clin Trials 2017;54:68–76. 10.1016/j.cct.2017.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Soltero EG, Konopken YP, Olson ML, Keller CS, Castro FG, Williams AN, et al. Preventing diabetes in obese Latino youth with prediabetes: a study protocol for a randomized controlled trial. BMC Public Health 2017;17(1):261. 10.1186/s12889-017-4174-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Soltero EG, Olson ML, Williams AN, Konopken YP, Castro FG, Arcoleo KJ, et al. Effects of a community-based diabetes prevention program for Latino youth with obesity: a randomized controlled trial. Obesity (Silver Spring) 2018;26(12):1856–65. 10.1002/oby.22300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Shaibi GQ, Konopken Y, Hoppin E, Keller CS, Ortega R, Castro FG. Effects of a culturally grounded community-based diabetes prevention program for obese Latino adolescents. Diabetes Educ 2012;38(4):504–12. 10.1177/0145721712446635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Shaibi GQ, Greenwood-Ericksen MB, Chapman CR, Konopken Y, Ertl J. Development, implementation, and effects of community-based diabetes prevention program for obese Latino youth. J Prim Care Community Health 2010;1(3):206–12. 10.1177/2150131910377909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Shaibi GQ, Konopken YP, Nagle-Williams A, McClain DD, Castro FG, Keller CS. Diabetes prevention for Latino youth: unraveling the intervention “black box”. Health Promot Pract 2015;16(6):916–24. 10.1177/1524839915603363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Castro FG, Alarcón EH. Integrating cultural variables into drug abuse prevention and treatment with racial/ethnic minorities. J Drug Issues 2002;32(3):783–810. 10.1177/002204260203200304. [DOI] [Google Scholar]
  • 55.Soltero EG, Ramos C, Williams AN, Hooker E, Mendez J, Wildy H, et al. Inverted exclamation markViva Maryvale!: a multilevel, multisector model to community-based diabetes prevention. Am J Prev Med 2019;56(1):58–65. 10.1016/j.amepre.2018.07.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Soltero EG, Ayers SL, Avalos M, Peña A, Williams AN, Olson ML, et al. Theoretical mediators of diabetes risk and quality of life following a diabetes prevention program for Latino youth with obesity. Am J Health Promot 2021. [DOI] [PMC free article] [PubMed]
  • 57.O’ Connor TM, Beltran A, Musaad S, Perez O, Flores A, Galdamez-Calderon E, et al. Feasibility of targeting Hispanic fathers and children in an obesity intervention: Papas Saludables Ninos Saludables. Child Obes 2020;16(6):379–92. 10.1089/chi.2020.0006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.O’ Connor TM, Perez O, Beltran A, Colon Garcia I, Arredondo E, Parra Cardona R, et al. Cultural adaptation of ‘Healthy Dads, Healthy Kids’ for Hispanic families: applying the ecological validity model. Int J Behav Nutr Phys Act 2020;17(1):52. 10.1186/s12966-020-00949-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Morgan PJ, Lubans DR, Callister R, Okely AD, Burrows TL, Fletcher R, et al. The ‘Healthy Dads, Healthy Kids’ randomized controlled trial: efficacy of a healthy lifestyle program for overweight fathers and their children. Int J Obes 2011;35(3):436–47. 10.1038/ijo.2010.151. [DOI] [PubMed] [Google Scholar]
  • 60.Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 2014;311(8):806–14. 10.1001/jama.2014.732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Garcia DO, Valdez LA, Hooker SP. Hispanic male’s perspectives of health behaviors related to weight management. Am J Mens Health 2017;11(5):1547–59. 10.1177/1557988315619470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Heerman WJ, Teeters L, Sommer EC, Burgess LE, Escarfuller J, Van Wyk C, et al. Competency-based approaches to community health: a randomized controlled trial to reduce childhood obesity among Latino Preschool-aged children. Child Obes 2019;15(8): 519–31. 10.1089/chi.2019.0064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Barkin SL, Heerman WJ, Sommer EC, Martin NC, Buchowski MS, Schlundt D, et al. Effect of a behavioral intervention for underserved preschool-age children on change in body mass index: a randomized clinical trial. JAMA 2018;320(5):450–60. 10.1001/jama.2018.9128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Castro FG, Barrera M Jr, Holleran Steiker LK. Issues and challenges in the design of culturally adapted evidence-based interventions. Annu Rev Clin Psychol 2010;6:213–39. 10.1146/annurev-clinpsy-033109-132032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Carey R, Jenkins E, Williams P, Evans F, Horan M, Johnston M, et al. A taxonomy of modes of delivery of behaviour change interventions: development and evaluation. European Health Psychol 2017.
  • 66.Clarke AR, Goddu AP, Nocon RS, Stock NW, Chyr LC, Akuoko JA, et al. Thirty years of disparities intervention research: what are we doing to close racial and ethnic gaps in health care? Med Care 2013;51 (11):1020–6. 10.1097/MLR.0b013e3182a97ba3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Walker RJ, Smalls BL, Campbell JA, Strom Williams JL, Egede LE. Impact of social determinants of health on outcomes for type 2 diabetes: a systematic review. Endocrine 2014;47(1):29–48. 10.1007/s12020-014-0195-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Mayan M, Lo S, Oleschuk M, Paucholo A, Laing D. Leadership in community-based participatory research: individual to collective. Engaged Scholar Journal 2016;2:11–24. [Google Scholar]
  • 69.Lee RM, Ramanadhan S, Kruse GR, Deutsch C. A mixed methods approach to evaluate partnerships and implementation of the Massachusetts prevention and wellness trust fund. Front Public Health 2018;6:150. 10.3389/fpubh.2018.00150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Dietz WH, Belay B, Bradley D, Kahan S, Muth ND, Sanchez E, et al. A model framework that integrates community and clinical systems for the prevention and management of obesity and other chronic diseases 2017;7. 10.31478/201701b. [DOI] [Google Scholar]
  • 71.About your YMCA. Valley of the Sun YMCA https://valleyymca.org/about-your-y/.2121.
  • 72.Brand T, Pischke CR, Steenbock B, Schoenbach J, Poettgen S, Samkange-Zeeb F, et al. What works in community-based interventions promoting physical activity and healthy eating? A review of reviews. Int J Environ Res Public Health 2014;11(6):5866–88. 10.3390/ijerph110605866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Economos CD, Hyatt RR, Goldberg JP, Must A, Naumova EN, Collins JJ, et al. A community intervention reduces BMI z-score in children: Shape Up Somerville first year results. Obesity (Silver Spring) 2007;15(5):1325–36. 10.1038/oby.2007.155. [DOI] [PubMed] [Google Scholar]
  • 74.Israel BA, Lachance L, Coombe CM, Lee SD, Jensen M, Wilson-Powers E, et al. Measurement approaches to partnership success: theory and methods for measuring success in long-standing community-based participatory research partnerships. Prog Community Health Partnersh 2020;14(1):129–40. 10.1353/cpr.2020.0015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Mendoza-Vasconez AS, Linke S, Munoz M, Pekmezi D, Ainsworth C, Cano M, et al. Promoting physical activity among underserved populations. Curr Sports Med Rep 2016;15(4):290–7. 10.1249/JSR.0000000000000276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Rombeek M, De Jesus S, Altamirano-Diaz L, Welisch E, Prapavessis H, Seabrook JA, et al. The use of smartphones to influence lifestyle changes in overweight and obese youth with congenital heart disease: a single-arm study: pilot and feasibility study protocol: Smart Heart Trial. Pilot Feasibility Stud 2017;3: 59. 10.1186/s40814-017-0207-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Mulgrew KW, Shaikh U, Nettiksimmons J. Comparison of parent satisfaction with care for childhood obesity delivered face-to-face and by telemedicine. Telemed J E Health 2011;17(5):383–7. 10.1089/tmj.2010.0153. [DOI] [PubMed] [Google Scholar]
  • 78.Laing SS, Ocampo P, Ocampo C, Caravalho J, Perez G, Baugh S. Provider perceptions of mHealth engagement for low-resourced, safety-net communities. Public Health Nurs 2021;38(1):13–21. 10.1111/phn.12811. [DOI] [PubMed] [Google Scholar]
  • 79.Yingling LR, Brooks AT, Wallen GR, Peters-Lawrence M, McClurkin M, Cooper-McCann R, et al. Community engagement to optimize the use of web-based and wearable technology in a cardiovascular health and needs assessment study: a mixed methods approach. JMIR Mhealth Uhealth 2016;4(2):e38. 10.2196/mhealth.4489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Hall AK, Cole-Lewis H, Bernhardt JM. Mobile text messaging for health: a systematic review of reviews. Annu Rev Public Health 2015;36:393–415. 10.1146/annurev-publhealth-031914-122855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Lenhart A. A majority of American teens report access to a computer, game console, smartphone and a tablet 2015. https://www.pewresearch.org/internet/2015/04/09/a-majority-of-american-teens-report-access-to-a-computergame-console-smartphone-and-a-tablet/2021.
  • 82.Partridge SR, Redfern J. Strategies to engage adolescents in digital health interventions for obesity prevention and management. Healthcare (Basel) 2018;6(3). 10.3390/healthcare6030070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Giovanelli A, Ozer EM, Dahl RE. Leveraging technology to improve health in adolescence: a developmental science perspective. J Adolesc Health 2020;67(2S):S7–S13. 10.1016/j.jadohealth.2020.02.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Radovic A, McCarty CA, Katzman K, Richardson LP. Adolescents’ perspectives on using technology for health: qualitative study. JMIR Pediatr Parent 2018;1(1):e2. 10.2196/pediatrics.8677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Raeside R, Partridge SR, Singleton A, Redfern J. Cardiovascular disease prevention in adolescents: eHealth, co-creation, and advocacy. Med Sci (Basel) 2019;7(2). 10.3390/medsci7020034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Andersson N Community-led trials: intervention co-design in a cluster randomised controlled trial. BMC Public Health 2017;17(Suppl 1):397. 10.1186/s12889-017-4288-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Chen JL, Guedes CM, Cooper BA, Lung AE. Short-term efficacy of an innovative mobile phone technology-based intervention for weight management for overweight and obese adolescents: pilot study. Interact J Med Res 2017;6(2):e12. 10.2196/ijmr.7860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Kebbe M, Perez A, Buchholz A, McHugh TF, Scott SD, Richard C, et al. End-user perspectives to inform policy and program decisions: a qualitative and quantitative content analysis of lifestyle treatment recommendations by adolescents with obesity. BMC Pediatr 2019;19(1):418. 10.1186/s12887-019-1749-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Thompson D, Cantu D, Bhatt R, Baranowski T, Rodgers W, Jago R, et al. Texting to Increase Physical Activity Among Teenagers (TXT Me!): rationale, design, and methods proposal. JMIR Res Protoc 2014;3(1):e14. 10.2196/resprot.3074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Thompson D, Cantu D, Ramirez B, Cullen KW, Baranowski T, Mendoza J, et al. Texting to increase adolescent physical activity: feasibility assessment. Am J Health Behav 2016;40(4):472–83. 10.5993/AJHB.40.4.9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Thompson DI, Cantu D, Callender C, Liu Y, Rajendran M, Rajendran M, et al. Photorealistic avatar and teen physical activity: feasibility and preliminary efficacy. Games Health J 2018;7(2):143–50. 10.1089/g4h.2017.0103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Sebire SJ, Jago R, Fox KR, Edwards MJ, Thompson JL. Testing a self-determination theory model of children’s physical activity motivation: a cross-sectional study. Int J Behav Nutr Phys Act 2013;10:111. 10.1186/1479-5868-10-111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Cadmus-Bertram L, Marcus BH, Patterson RE, Parker BA, Morey BL. Use of the fitbit to measure adherence to a physical activity intervention among overweight or obese, postmenopausal women: self-monitoring trajectory during 16 weeks. JMIR Mhealth Uhealth 2015;3(4):e96. 10.2196/mhealth.4229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Richmond TK, Thurston IB, Sonneville KR. Weight-focused public health interventions-no benefit, some harm. JAMA Pediatr 2021;175(3):238–9. 10.1001/jamapediatrics.2020.4777. [DOI] [PubMed] [Google Scholar]
  • 95.Chai LK, Collins CE, May C, Brown LJ, Ashman A, Burrows TL. Fidelity and acceptability of a family-focused technology-based telehealth nutrition intervention for child weight management. J Telemed Telecare 2021;27(2):98–109. 10.1177/1357633X19864819. [DOI] [PubMed] [Google Scholar]
  • 96.Chen JL, Weiss S, Heyman MB, Cooper B, Lustig RH. The efficacy of the web-based childhood obesity prevention program in Chinese American adolescents (Web ABC study). J Adolesc Health 2011;49(2):148–54. 10.1016/j.jadohealth.2010.11.243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Masse LC, Vlaar J, Macdonald J, Bradbury J, Warshawski T, Buckler EJ, et al. Aim2Be mHealth intervention for children with overweight and obesity: study protocol for a randomized controlled trial. Trials 2020;21(1):132. 10.1186/s13063-020-4080-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Appel HB, Huang B, Cole A, James R, Ai AL. Starting the conversation - a childhood obesity knowledge project using an app. Br J Med Med Res 2014;4(7):1526–38. 10.9734/bjmmr/2014/5512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Thompson D Incorporating behavioral techniques into a serious videogame for children. Games Health J 2017;6(2):75–86. 10.1089/g4h.2016.0066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Brigden A, Anderson E, Linney C, Morris R, Parslow R, Serafimova T, et al. Digital Behavior change interventions for younger children with chronic health conditions: systematic review. J Med Internet Res 2020;22(7):e16924. 10.2196/16924. [DOI] [PMC free article] [PubMed] [Google Scholar]

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