Introduction
Obesity and related chronic conditions impose a disproportionate burden on racial and ethnic minority adults in the United States (U.S.), exacerbating health inequities and straining healthcare systems. According to Centers for Disease Control and Prevention (CDC) data, non-Hispanic Black adults exhibit the highest obesity prevalence (49.9%), followed by Hispanic adults (44.8%), compared to non-Hispanic White adults (42.2%).1 These elevated rates contribute to chronic disease disparities that reduce quality of life and impose an estimated annual economic cost of more than $173 billion. Lifestyle interventions targeting diet and physical activity remain a cornerstone for weight management, yet adherence is notably lower among racial and ethnic minority participants than among non-Hispanic White counterparts, often reducing intervention effectiveness.2,3 Well-documented structural and social barriers are additionally at play, including limited access to affordable, healthy foods, scarcity of safe physical activity spaces, demanding work schedules, and competing familial obligation factors consistently challenging weight management in minority populations.4,5
Public health researchers have examined culturally targeted interventions (developed for specific groups) and tailored adaptations (customized to specific individuals), with evidence indicating that these approaches can improve engagement, retention, and outcomes by aligning with cultural values and personal experiences.3–5 Electronic and mobile health (e/mHealth) platforms have emerged as particularly promising vehicles to facilitate such strategies. They offer expandable, low-cost delivery that overcomes geographic and temporal barriers while enabling personalization through real-time monitoring and feedback.6 The e/mHealth landscape has evolved rapidly, from early accelerometers for activity tracking to sophisticated smartphone apps delivering adaptive behavioral recommendations. For instance, recent work demonstrates that apps logging historical data can increase physical activity adherence by up to 25% through visual progress cues and reminders.7
Systematic reviews affirm e/mHealth’s efficacy for weight loss and maintenance in general adult populations, yielding modest weight reductions (2-5 kg) via self-monitoring and coaching.8–10 However, only 2 prior reviews have focused on U.S. racial and ethnic minorities. The first narratively summarized 6 eHealth studies, noting short-term, low-magnitude losses but calling for extended trials.11 The second analyzed 16 studies, highlighting the potential for public health behavior change in underserved groups and noting the need for continued research in this area.12 These syntheses, now almost a decade old, predate pivotal technological advances like wearable data collection, integration and AI-driven personalization, leaving a critical void in the understanding of e/mHealth’s potential to address equity amid increasing health disparities. Adding to these challenges, much of the literature focuses on individual behaviors such as self-tracking, with limited attention to how social determinants of health (SDOH), including cost, digital access, and community support, shape whether participants can realistically engage with these tools.
Some reviews suggest that weight-management interventions yield smaller effects, often 20%-30% lower, among racial and ethnic minority participants when programs fail to address contextual barriers such as resource access, social support, or environmental challenges.13 The current scoping review extends prior work by mapping contemporary evidence (through June 2025) to clarify how e/mHealth interventions address SDOH and ethnic subgroup needs, while assessing their cultural responsiveness and scalability across diverse populations. Accordingly, this review had 4 aims: (1) to summarize the diversity of study designs and intervention approaches, (2) to describe the extent of cultural tailoring and community engagement, (3) to identify critical gaps in the evidence base, particularly where studies have not adequately examined how systemic barriers faced by underrepresented groups may influence participation, engagement, or outcomes, and (4) to propose priorities for future research to advance health equity in obesity prevention among U.S. racial and ethnic minority adults.
Methods
The study protocol was originally registered with PROSPERO (Registration: blinded for review) and subsequently published on Open Science Framework as a scoping review protocol (blinded for review). This scoping review followed the Joanna Briggs Institute (JBI) framework and built on Arksey and O’Malley’s methodology for rigor.14 The PICOS (population, intervention, comparison, outcome, and study type) method was used to formulate the search strategy by identifying the key concepts necessary for study inclusion. Reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and related scoping review guidance (PRISMA-ScR).15–17
Search strategy
In collaboration with a research librarian (EW), the authors developed a comprehensive MEDLINE search strategy using article titles, abstracts, and index terms, which was subsequently adapted for other databases (see Table S1). Only English-language, peer-reviewed journal articles were included. Following a pilot test of 50 records, the search criteria, selection processes, and data extraction forms were refined, resulting in minor protocol amendments (eg, clarification of the definitions of e/mHealth modalities to include emerging hybrids such as app, community health worker (CHW) integrations; full details are provided in Table S2).
The final strategy incorporated both text words and subject headings related to National Institute of Health (NIH)-designated U.S. health disparity populations, overweight and obesity, e/mHealth interventions, and weight management or loss. Each database was searched from inception through June 2025, without publication-year limits. Conference proceedings, abstracts, editorials, theses, dissertations, and commentaries were excluded.
Forward and backward citation searches of included studies were conducted to supplement database results.
Databases
The authors searched MEDLINE (Ovid), Cochrane (Wiley), Embase (Elsevier), CINAHL Plus (Ebsco), and PsycINFO (Ebsco).
The search terms included “electronic health intervention,” “mobile health intervention,” “weight loss,” “weight management,” “overweight,” “obesity,” “ethnic minorities,” and “U.S. health disparity populations.” The full Boolean string is in Table S2.
Inclusion criteria were set for population, intervention, comparison, outcome study type, and context. Based on population, adults aged ≥18 years with overweight or obesity (Body Mass Index [BMI] ≥25 kg/m²) at baseline and ≥50% identifying as a racial/ethnic minority per NIH designations (Black, Hispanic/Latino, American Indian/Alaska Native, Asian American, or Native Hawaiian/Other Pacific Islander) were included. Studies had to include intervention with ≥1 e/mHealth modality (eg, apps, Short Message Service (SMS), and wearables).
Weight had to be an outcome of studies included, and study types included were experimental/quasi-experimental designs, including RCTs, non-randomized controlled trials, and single-group pre/post studies that were U.S.-based. All articles included were English-language peer-reviewed articles.
Selection process
Records were managed in EndNote and screened independently in Rayyan by 2 reviewers; discrepancies were adjudicated by a third (RB, CA, SB, SM, CN, and AFP). The search was conducted in 2 epochs: inception-January 2022 and February 2022-June 2025. The process was tracked according to Cochrane18 and PRISMA-ScR17 guidelines.
Data extraction and synthesis
Two reviewers (RB and CA) used a modified Cochrane tool,18 and each trialed the tool on 5 studies; this process was then refined to improve efficiency. Data were extracted to include publication details, study/participant characteristics, and intervention specifics (eg, duration, technologies, cultural tailoring, and use of any co-design) as reported by the paper reviewed. Given heterogeneity in study design, measurements used, intervention approaches and outcomes, findings were summarized using qualitative narrative synthesis.14,16
Results
The two search epochs yielded 4811 unique records; 51 studies met the inclusion criteria after full-text review (See Figure 1). The characteristics of these 51 studies were captured (see Table S3) and subsequently analyzed. These studies were predominantly RCTs (45), as broken down below.
Study design description, interventions, and follow-up
Among RCTs, 18 were pilot studies, 8 were feasibility trials, and 5 were a combination of both. Of the 6 pre-post designs, 2 were feasibility studies, and 1 was a pilot study. The study sample sizes ranged in number from 17 to 802.
Samples included male and female adults, postpartum women, and parents of children living with obesity, young adults, college students, and adults living in rural regions such as the Lower Mississippi Delta.19 Most studies (86%, n = 44) used usual or standard care as the comparator, while 14% (n = 7) lacked control groups, with 4% (n = 2) randomized participants to multiple active intervention arms.
Few studies explicitly cited a behavior theory framework to guide intervention design (n = 7), most commonly Social Cognitive Theory2,20–23 or Self-Determination Theory;24,25 the remaining 86% did not name any guiding theory. Twenty-one studies focused exclusively on African American/Black participants, followed by Latinas/Hispanics (10%, n = 5) and Filipino Americans (4%, n = 2). The remaining studies (45%, n = 23) examined mixed-race samples (eg, African American, Hispanic, White, Asian, Other), often majority Black/non-Hispanic (18%, n = 9) or Hispanic (10%, n = 5), including Mexican-origin groups (4%, n = 2) and 1 study with combined African American (37%) and Hispanic (18%) participants (see Table 1).
Four studies (8%) also featured Puerto Ricans or Mexican Americans.3,26,27 Participants were predominantly female (75%, n = 38, eg, 88% female8) and urban (80%, 41 studies in clinic-based settings). Almost 25% (n = 23) included or targeted adults >60 years of age, including older Black women in text message, supported walking interventions.20,28 Only 8% (n = 4) included exclusively male participants, with 4% (n = 2) featuring >50% males.3,27–29
Few studies lacked follow-up protocols (8%, n = 4), with most including post-baseline assessments of varying durations (92%, n = 47). Active intervention durations ranged from 5 weeks to 104 weeks, with 6 months being the most frequent (31%, n = 16) (Table S3; Figure 2). True post-intervention follow-up assessments (conducted after the active intervention ended) were less common, with only 12 articles (24%) including assessments beyond 12 months post-baseline.
Overall, the duration of follow-up ranged from 3 to 24 months, with 6 months being the most frequent (53% of cases) (Figure 2).
Intervention durations, focus, and weight outcomes
The active intervention durations ranged from 5 weeks to 2 years, with 6 months (24 weeks) being the most common active intervention length (31%, n = 16). Out of 51, 41% (n = 21), primarily targeted weight loss, 10% (n = 5) focused on weight maintenance or prevention of gain, and most (49% n = 25) addressed multiple health behaviors (eg, weight + physical activity and/or monitoring metabolic markers) (Table 1).
In the studies targeting weight loss, about half reported statistically significant weight loss, with effects typically in the small to moderate range (1-3 kg or 2%-5% body weight). Larger losses were observed in incentivized designs (eg, 4.79 kg30) However, these significant weight losses were mostly short-term, with regain or loss of statistical significance as compared to a variety of comparators during assessment beyond 12 months.
To assess relative effectiveness, nearly half of the RCTs compared e/mHealth interventions with non-e/mHealth approaches. E/mHealth interventions generally outperformed controls in over half of comparisons, while approximately one-third showed no significant difference, and were inferior in almost 15% (n = 7), typically due to low adherence (see Table 1). Comparators varied widely across studies, from low-intensity brief referral or counseling in usual primary care to more structured behavioral programs, such as in-person Diabetes Prevention Program (DPP).
E/mHealth interventions were more likely to show benefits when compared with usual care, whereas comparisons with higher-intensity programs generally produced smaller or mixed effects.
Across the included studies, 78% (n = 40) were primarily mHealth interventions (apps, SMS, wearables), and achieved significant outcomes in 48% of cases (n = 19). Non-mHealth interventions (web or interactive voice response) comprised 22% (n = 11), and they achieved significant outcomes in 45% of cases. The modest difference was driven by higher retention in mHealth’s interventions (82% vs.
71%), although effect sizes were similar in the few head-to-head trials in 8% (n = 4).
Cultural sensitivity in program designs and interventions
In terms of tailoring availability, 67% (n = 34) employed interventions that featured cultural, language, or content adaptations, and some were tailored to specific ethnic subgroups (eg, Filipino food photos into a Tagalog pamphlet).31 Others adapted texts and materials to improve cultural fit.3,26,27 Yet engagement in co-design with participants remained low (6%, n = 3), with protocols driven by implementers rather than communities.
Overview of e/mHealth weight loss intervention components
Common e/mHealth components included SMS (35%, n = 18), accelerometers (29%, n = 15), video calls (24%, n = 12), phone support (20%, n = 10), social media (16%, n = 8), tracking apps (39%, n = 20), and online coaching or education (49%, n = 25) (see Table 1), with most studies combining 2 or more (59%, n = 30). Intervention components included both the e/mHealth technology platforms (eg, apps, SMS, wearables) and the behavior change strategies employed through them (eg, self-monitoring, goal setting). Broader social elements referred to support beyond the individual level, such as family or peer networks, community resources, or other linkages (eg, Facebook or WIC programs).
Earlier studies (60% of pre-2020) relied more on pedometers or accelerometers, while recent studies shifted to apps or SMS hybrids (39%).
Interventions that included strategies to address extra-individual or structural barriers, such as community engagement activities or resource support, were relatively uncommon (20%, n = 10), even though these factors often shape participants’ ability to engage with e/mHealth tools. All 10 studies testing such interventions reported high engagement rates (75%-90%). These interventions incorporated multi-level strategies, meaning elements that extend beyond individual behavior change, such as peer support networks (eg, Facebook groups in 8%, n = 4) or linkage to policy and community resources,32 including examples such as a church-based intervention22 and a Women–Infant–Children (WIC)-supported program.33 A large majority of interventions (n = 44) emphasized individualized feedback and self-tracking.
In comparison, the 20% (n = 10) with a broader social elements, such as family or peer networks, community resources, or linkages like Facebook groups or WIC programs, reported high engagement rates of 75%-90%. No reviewed studies incorporated artificial intelligence (AI) or machine learning for personalization.
Discussion
This scoping review synthesized evidence from 51 U.S.-based e/mHealth interventions for weight management among racial/ethnic minority adults. Although most trials were narrowly focused on targeting individual behavior change, multi-level interventions that incorporate family, community, or structural supports to enhance individual behavior change are emerging. Overall, nearly half of the studies reported significant but modest short-term weight reductions (1-3 kg), with hybrid interventions (multiple modalities) targeting behaviors outperforming single-focus approaches (49% vs.
41%).
Few studies drew on explicit theoretical frameworks (14%; see Results), such as Social Cognitive Theory2,19–22 or Self-Determination Theory,23,24 which limited replicability amid inconsistent reporting (occurring only 6%, n = 3). Heavy reliance on usual-care controls (86%) and on designs without controls (14%) weakened causal inferences, underscoring the need for formidable comparators. By rarely linking individual behaviors (eg, app self-monitoring) to contextual or cultural factors (eg, community norms or SDOH), or other psychosocial determinants, current approaches limit the understanding of how digital interventions can be effectively tailored for diverse populations.
Aligning with our objectives, the evidence affirms the moderate e/mHealth effectiveness for behavioral change, but the lack of sustained follow-up and insufficient SDOH integration caution against its generalizability in the absence of multi-level adaptation.
Cultural and engagement insights
Over two-thirds of studies incorporated culturally sensitive elements, such as language adaptations or group-specific content, and several reported higher retention or more consistent participation in these tailored arms compared with untailored or standard of care conditions. The predominance of African American/Black (41%, n = 21) and Hispanic (10%, n = 5) samples, with a sparse Filipino/Asian focus (6%, n = 3), reinforces the underrepresentation of certain minorities and ethnic subgroups, which again limits transferability. Only 6% (n = 3) included participant involvement in design, planning, implementation, or evaluation, which potentially limits their effectiveness and scalability.
Among these co-designed studies, male participation remained very limited, consistent with the overall underrepresentation of men across the 51 studies. While specific attrition/adherence rates for these 3 studies were not consistently reported, they generally demonstrated high engagement (eg, 80%-90% retentions in Rosas et al., 27 compared to more variable rates in non-co-designed studies). Co-designed interventions, as demonstrated in community-led lifestyle programs, enhance cultural fit and sustained uptake by 20%-30% among minorities.32
Cultural tailoring appeared to produce higher retention (eg, 85%-96% completion rates in studies3,28,31,34) but most study customization relied on researchers’ untested assumptions rather than utilizing proven methods or theoretical frameworks (eg, validated scales), potentially limiting replication and expandability. A pronounced female skew (∼75%, n = 38) has left groups, such as Hispanic men, underserved.8 Targeted efforts, for example the HOMBRE program, a 12-month coach-facilitated intervention with flexible virtual/in-person sessions and MyFitnessPal tracking for Latino men, yielded high retention rates (80%-90%) out to 18 months.27 Similarly, faith-based virtual curriculum for Black men (1.0-2.0 kg loss, P < .05) offers a model for subgroup-specific strategies.29,35 Such focused approaches, attuned to intra-ethnic differences in diet or access, are essential for reducing disparities.
Yet, the scarcity of male-only samples and/or interventions intentionally tailored for men, highlights a broader gap: men’s limited involvement exacerbates obesity inequities and chronic disease burdens. Expanding family-centered models that leverage kinship and shared accountability34 could improve engagement among minority men, shifting e/mHealth from stand-alone individual tools toward models where the technology is integrated into closed-loop social, clinical, and/or community-embedded approaches (eg, CHWs using digital tools to support patients using mHealth tools as an individual or family).32,36 Informed by co-design (only 6%, n = 3), such approaches would align interventions with the everyday experiences and the cultural contexts of minority families, promoting equity across genders and attending to intra-ethnic variations. Future trials emphasizing co-designed, family-inclusive strategies could make digital interventions more sustainable and socially grounded.
Technology and outcomes
e/mHealth tools evolved from pedometers (29%, n = 15) to integrated apps/SMS (39%, n = 20). Frequent contact protocols with coaching or feedback (eg, technology-assisted health coaching embedded in primary care37) consistently outperformed low-touch approaches in nearly half of the trials. The modal active intervention duration of 6 months (31%, n = 16) and the relative rarity of assessments beyond 12 months (24%, n = 12) highlight the methodological constraints in drawing firm conclusions on intervention durability.
Community health worker-enhanced programs offer 1 strategy to mitigate barriers to digital tool use, blending human support with technology to build trust and improve accessibility. For instance, a study that paired virtual plant-based texts with community health worker (CHW) discussions on cooking and challenges created a supportive space for Black participants to sustain changes.32 Similarly, another study combined app feedback with CHW guidance in a Hispanic community, helping users navigate the tool while addressing real-life hurdles like time or comfort with technology.36 These efforts highlight how partnerships can ease the digital divide, particularly for older populations,29 where usability issues like interface complexity often persist.38 Embedding CHWs and culturally competent staff in digital programs appears critical for ensuring inclusivity and sustained engagement.
In contrast, low-intensity trials and single component interventions often fell short, underscoring the limits of standalone tools without adequate support. For example, other studies noted activity gains but no weight loss from exergaming or minimal prompts, revealing challenges in keeping participants motivated without enablers, such as coaching or context.39,40 As with any program, the cost and burden of such tools should be taken into account such that tools can be accessed and used by the target population. These non-significant findings underscore a considerable challenge; isolated e/mHealth components struggle to surmount the systemic barriers that disproportionally affect minority communities.
Simply minimizing interventions or tools, while they use less resources, may not be the answer. Future work could use the Multiphase Optimization Strategy37 to optimize the use of such tools to find effective components and component combinations that can be used under a priori determined resource constraints.
Approaches addressing SDOH, though rare, offer hope. Some studies expanded beyond weight to metabolic markers like Hemoglobin A1c, framing obesity as a chronic disease.21,26,41 Another study used Spanish-language counseling and text messages to tackle food access, resulting in weight loss.42 Incentives and hybrid approaches offer another promising path: 1 trial rewarded step and weight goals with cash, producing larger weight losses,30 while another paired apps with community health workers to address access gaps.43 These examples suggest that tackling barriers such as affordability, digital literacy, and offering sustained support, rather than focusing solely on technology, offers a stronger blueprint for achieving health equity. No studies included AI or machine learning (eg, predictive algorithms or adaptive learning models) for personalization of intervention content or feedback though we suspect that this will be a prolific area of intervention science in the very near future.
Strengths and limitations
Adhering to JBI methodology, this review broadens prior syntheses11,12 with 2022-2025 evidence and diverse designs. Limitations include small/urban/female-biased samples (eg, 87.8% female in some), short durations (<6 months in 53%), and inconsistent outcomes, (eg, varying metrics such as absolute kg loss or sustainment vs. % body weight reduction, and different follow-up durations), making it difficult to combine data from multiple studies for a meta-analysis. Asian subgroups remain underrepresented (4%), and broad categories obscure needs like Mexican vs.
Puerto Rican variations, spurring calls for more detailed data. Moreover, our U.S.-only scope does not capture insights from weight loss interventions among immigrant and refugee communities in other high-income settings, such as Somali women in Norway,44 which may offer additional perspectives on cultural tailoring and migration-related barriers. External validity is also constrained by the small effect sizes in most trials likely due to short durations (modal 6 months), individual-level focus (86%) of studies, and limited SDOH integration (20%), which may limit the ability to overcome systemic barriers faced by racial and ethnic minority participants.
Indeed, public health research and practice on the whole lacks attention to a multitude of contextual, socioeconomic, and structural barriers45 and our findings reflect such. Potential biases include selection (urban/recruitment skew) and imprecise engagement metrics.
Implications and future directions
e/mHealth interventions show moderate promise for addressing obesity disparities among racial and ethnic minority adults in the U.S.; however, most trials rely solely on individual behavior-change strategies (eg, self-monitoring or app use) and give limited attention to the social and structural factors, such as cost, digital access, neighborhood resources, and social support, that strongly influence whether participants can engage with these tools and if intended effects of using such tools is realized. First, future research should prioritize co-designed, culturally responsive interventions that reflect the real experiences of target populations and enhance both relevance and engagement. Second, these interventions should combine digital tools with community supports, such as community health workers, peer networks, or local organizations, and sustained follow-up, as emerging evidence suggests these elements can strengthen engagement in intervention and maintenance of effects.37,46 Third, advancing health equity will require coordinated, multi-level, long-term studies developed in partnership with communities to overcome systemic barriers to obesity prevention and management and ensure equitable impact across diverse populations.
Finally, using theories, models, and frameworks, consistent constructs and measures, and well specified interventions and their components among such studies could enable meta-analyses to be conducted. As well, generalizability could be improved even in the face of adaptations needed for various contexts, particularly if consistent adaptation reporting were adopted. Together, these efforts can ensure that e/mHealth becomes more than a temporary tool for weight management; it can serve as a sustainable, community-driven approach to improving metabolic health and reducing long-standing obesity disparities among racial and ethnic minority adults in the U.S.
Supplementary Material