Evaluating the effects of mobile health interventions in the emergency department to improve patient health behaviors: a literature review

Introduction

Mobile health (mHealth) or digital health refers to the use of mobile devices and technologies including smartphones, tablets, applications and text messaging to deliver healthcare services to promote communication and management (1). mHealth is a subset of the umbrella term ‘telehealth’ which is the use of technology to deliver remote healthcare. Telehealth is operated mainly by four channels: live video conferencing, remote patient monitoring, store-and-forward and mHealth (2). It operates as an alternative to traditional office visits and provides synchronous and provider-centric virtual contact. mHealth, in specific, relies on smartphones, tablets and wearable technology to support patient health. Beyond existing as a mode of communication, mHealth has the potential to provide support over a spectrum of interventions that address both physical and mental health conditions such as diabetes, hypertension, anxiety, depression and substance use disorders.

mHealth technologies have rapidly evolved over the past decade, offering scalable, cost-effective solutions to improve health care delivery, patient engagement, and chronic disease management. These benefits have been seen in a diverse set of clinical environments. In community settings, apps are available to track diet and fitness; in acute care, short messaging service (SMS) applications facilitate post-discharge follow up; and in hospital-based care, inpatient visitors can view lab results and schedule appointments (3-5). In general, mHealth interventions have shown potential in improving medication adherence, self-management behaviors, appointment attendance, and access to preventive care, especially when tailored to individual needs and cultural contexts (6-9). However, despite the improvements in patient behavior, mHealth tools are largely underutilized across different healthcare settings. While the utility of mHealth in primary care and outpatient settings is well documented, its implementation within the emergency department (ED) and its ability to affect patient behavior remains an emerging field.

To better understand the potential impact of mHealth within the ED, it is important to first comment on the unique role the ED plays in healthcare. EDs are open 24/7 and are federally mandated under the Emergency Medical Treatment and Labor Act (EMTALA) to provide care regardless of insurance status, rendering them uniquely accessible to vulnerable populations (10). With these protections set in place, marginalized patient populations who may benefit most from these targeted health interventions often rely primarily on the ED for primary care and all healthcare needs regardless of acuity (11). Recent data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) indicates that Medicaid recipients visit the ED at a rate of about 99 per 100 people compared to 21 per 100 for those with private insurance (12). These populations often face a double burden of having lower rates of routine primary care follow-up yet face disproportionate rates of chronic conditions, such as diabetes and hypertension, than the general population (13). ED visits present a missed opportunity for preventative health outcomes that are often attributed to patient, provider and institutional barriers (14).

However, EDs present their own challenges, with soaring patient volumes, time constraints, and prioritization of staff and resources for high acuity or unstable patients (15). Recent data shows a steady uptick in annual, total ED visits from the 1990s to the 2020s (16,17). In 2021 alone, 140 million ED visits occurred in the United States (U.S.) (18) and in 2022, the Centers for Disease Control and Prevention (CDC) estimated the highest volume ever reported at 155.4 million visits, as well as an all-time high utilization rate of 473 visits per 1,000 populations (19). In comparison, ED visits in 2012 were about 131 million, which was 424 visits per 1,000 population.

Digital interventions offer a promising method in bridging the gap between acute care and long-term management. Specific to emergency medicine, mHealth has gained traction in its potential to optimize workflow, enhance patient care, and reduce the burden on clinicians who often lack the time for lengthy discussions about chronic disease management. For example, studies have shown that among patients presenting with “low-acuity” complaints to the ED, there is an increasing willingness from clinicians to use mHealth technology in medical triages, which can result in decreased visits (20). Other works have shown that through text messaging and mobile applications, mHealth can effectively deliver tailored health tips, appointment reminders, and educational content on topics such as smoking cessation, chronic disease management, and medication adherence (21). Additionally, the utility of these systems extends beyond patient education. mHealth tools can support clinical decision-making by collecting patient-reported data in real time and generating tailored recommendations informed by a patient’s medical history and current systems (22). However, there is little research available evaluating the integration of mHealth in the ED to promote operations and patient behavior. This study aims to add to this concept and research on why digital tool integration is justified.

Beyond improving clinical efficacy and workflow, the integration of these technologies holds implications for addressing broader systemic issues in healthcare, as digital health may be well-suited to specially help the underserved who often rely on the ED for access to care. For example, mobile interventions provide low-cost, automated self-management that can reduce reliance on expensive acute care (23). Additionally, remote monitoring reduces the frequency of required in-person visits following ED discharge by allowing patients to manage conditions from home (24). Vulnerable populations in the U.S., including lower-income groups, non-English speakers, and those with low health literacy, may particularly benefit from multilingual, user-friendly and multimedia digital tools that provide accessible health information (25,26).

Given the novel convergence of high-risk populations in the ED and the growing viability of digital tools, a systematic evaluation of the current mHealth landscape is important towards canvassing the current interventions being effectively implemented nationally. Although mHealth has been studied in various clinical settings, few reviews have focused specifically on its use in the ED. In addition, few reviews incorporated a robust inclusion of randomized controlled trials (RCTs), assessed the internal validity of papers (particularly those that may include behavioral theory), and explored studies that include varying sample sizes to detect a significance difference. Therefore, the goal of this study was to (I) examine U.S.-based mHealth interventions initiated in the ED, (II) identify key domains where these tools are applied, and (III) assess their effectiveness in addressing chronic disease management, care continuity, and health behavior change (27). By focusing on RCTs, we aimed to enhance the internal validity of our synthesis by minimizing bias and creating more accurate causal inferences of treatment effects. In addition, many of our included studies were framed around behavioral health theory (e.g., the Health Belief Model or Self-Determination Theory) which provides insights to change mechanisms like goal setting, self-efficacy, and social support. This helps guide developers as interventions are created and modified to tailor effectiveness, improve user engagement and adherence. Most studies contained robust sample sizes and explored low-cost, emergent and scalable tools (e.g., phone, text) to increase public health appeal. Additionally, included studies largely targeted underserved populations to address health disparities, demonstrating important potential to address inequities. We present this article in accordance with the Narrative Review reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-63/rc).

Methods

Study design

We conducted a narrative review of U.S.-based RCTs evaluating mHealth interventions in the ED and their impact on patient health behaviors. This allows for qualitative assessment of the current, national landscape of digital health in the ED.

Eligibility criteria

To ensure robustness and relevance, we set specific inclusion parameters to isolate applicable studies set in a U.S. healthcare context. Studies were eligible if they: (I) were peer-reviewed RCTs of any mobile or digital health intervention initiated in an ED setting in the U.S.; (II) reported patient-oriented outcomes (e.g., outcomes centered on patients rather than providers or implementation); (III) were published in English; and (IV) appeared between June 2014 and June 2024. We prioritized RCTs to ensure evidence supporting intervention efficacy. We also constrained our review to U.S.-based studies to maintain applicability to the U.S. emergency care system. Finally, a ten-year timeframe was set to focus on recent and relevant advancements in digital tools. Pilot RCTs were excluded to focus the review on powered studies that could determine efficacy.

Search strategy

With these inclusion parameters defined, we conducted a search strategy to locate relevant studies. We searched three electronic databases, PubMed, Scopus, and the Cochrane Database of Systematic Reviews, using a combination of Medical Subject Headings (MeSH) terms, text words, and title/abstract keywords grouped into two main concepts: (I) mHealth and (II) ED (see Table 1).

Study selection

Following the search execution, we screened and selected studies from each of the chosen databases. This involved the primary screening of titles and abstracts and removal of duplicate studies. M.A. conducted a non-blinded search and screening of articles. M.A. and L.S.C. discussed potential disagreements of study inclusion with J.Z. acting as the tiebreaker after careful assessment to improve consistency and accuracy. The PubMed search initially identified 2,023 records; after filters for clinical and randomized trials were applied to adhere to the study parameters, 148 remained. Of these, 42 were reviewed in full. The Cochrane search yielded 17 records, none of which met eligibility. The Scopus search identified 82 studies, 18 of which were reviewed for eligibility. These findings were adapted into the PRISMA 2020 flowchart (see Figure 1) (28). We also screened high-impact emergency medicine journals to ensure completeness and ensured no missing studies from our database searches.

Figure 1 PRISMA flow chart. RCT, randomized controlled trial.

Data management

Once the final set of eligible studies were identified, we implemented a structured system to organize and analyze the collected papers. Data was extracted and charted by study characteristics (e.g., author, title, year, and aim of study) as well as intervention design, study design, participant descriptions and study outcomes. All references were imported into Zotero for citation management and into a structured Excel spreadsheet for tracking study characteristics, inclusion status, and notes.

Reporting standards

To validate the methodological quality of this process, we adhered to the established reporting standards. We collated results through a narrative synthesis and grouped by thematic health behaviors (e.g., injury prevention, substance use, chronic diseases, etc.). Across studies, the efficacy was evaluated to determine overall implications of utilizing mHealth tools under specific ED domains.

Results

We identified 21 RCTs that evaluated mHealth interventions in the ED. These were categorized into five domains: (I) substance use, (II) chronic disease and medication adherence, (III) linkage to care, (IV) sexual health, and (V) safety and injury prevention. We discuss these below in the order of their study frequency.

Substance use (n=6 studies)

Substance use including tobacco, alcohol, and illicit drugs was the most common focus of mHealth interventions and remains a major public health concern in the U.S.. Smoking and alcohol are directly associated with increased morbidity and mortality from respiratory illness, cardiovascular disease, liver disease, and various cancers (29-31). These harms fall disproportionately on underserved groups, including uninsured or underinsured patients, racial and ethnic minorities, and individuals experiencing homelessness, thereby widening existing health inequities As patients with substance use are often frequent ED utilizers (32), the ED represents a critical setting to deploy mHealth interventions to support treatment engagement and reduce future acute care use. Consequently, digital tools such as mobile apps, SMS messaging, and computer-delivered programs have been applied to address substance use in high-risk populations.

Demonstrating the efficacy of SMS-based interventions, Suffoletto et al. conducted two RCTs to reduce hazardous drinking among young adults at urban hospitals in Pittsburgh, Pennsylvania. In a 2014 study adopting the Theory of Reasoned Action, Health Belief Model and Information Motivation, 765 young adult ED patients were randomized into three groups: (I) SMS assessments with feedback on binge drinking (SA + F), (II) SMS assessments only (SA) or (III) a control group (33). At 3-month follow-up, the SA + F group showed a reduction in binge drinking days [odds ratio (OR) =−0.51; 95% confidence interval (CI): −0.10 to −0.95], while the SA group experienced an increase in binge drinking (OR =0.90; 95% CI: 0.23–1.6). Building on this work, Suffoletto et al. published in 2023 a five-arm RCT evaluating various behavioral change techniques to reduce hazardous binge drinking in non-treatment seeking young adults (34). Among five texting intervention arms [self-monitoring (TRACK), pre-drinking plan (PLAN), consumption feedback (USE), goal setting (GOAL) or a combination (COMBO)], COMBO yielded the greatest reduction in binge drinking from baseline to 3-month follow-up (OR =3.0 to 2.3; 95% CI: −0.77 to −0.26), while TRACK showed an increase (OR =2.7 to 3.4). These interventions utilized Self-Regulation and Planned Behavior theories.

While SMS tools show promise, computer delivered interventions can also be leveraged to address substance use. Vaca et al. implemented an automated bilingual computerized alcohol screening intervention (AB-CASI) among 840 self-identified Latino ED patients with unhealthy drinking habits from an urban community tertiary care center in the Northeastern U.S. (35). At 12-month follow-up, participants in AB-CASI experienced reductions in binge drinking episodes within the last 28 days (OR =3.2; 95% CI: 2.7–3.8) compared to the standard group (OR =4.0; 95% CI: 3.4–4.7), with a notable 30% reduction observed in patients over 25 years old [relative difference (RD) =0.70; 95% CI: 0.54–0.89].

Shifting from alcohol to opioid use, Gustafson et al. assessed the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), a self-determination theory-based smartphone application offering motivational support and recovery tools alongside medication for opioid use disorder (MOUD) in a sample of 414 adults with moderate to severe opioid use disorder (OUD) (36). While there was no overall significant difference in opioid abstinence between the MOUD + A-CHESS and MOUD-only groups (OR =1.10; 95% CI: 0.90–1.33), the intervention did show an impact on those taking methadone (OR =0.57; 95% CI: 0.34–0.97) or those experiencing fewer withdrawal symptoms (OR =0.95; 95% CI: 0.91–1.00).

For marijuana use, Waller et al. explored the effectiveness of a computer-delivered brief intervention (CBI) in reducing cannabis use among 237 adult ED patients in a low-income urban setting (37). This CBI, guided by a virtual counselor through motivational interviewing, focused on three domains which are a goal for change, identifying personal strengths, and evoking change. The evoking change domain yielded the greatest reductions in marijuana use at 6 months [2.91 fewer days in usage, standard error (SE) =1.10, P<0.01]. Within this domain, concerns regarding the impacts of marijuana use on family and friends motivated even greater change (5.5 fewer days in usage, SE =1.63, P<0.01).

Regarding tobacco cessation, Arana-Chicas et al. developed Decídetexto, a bilingual text-message intervention to support smoking cessation among 1,680 Latino or Hispanic smokers over 21 years in an ED in Northern New Jersey (38). Both phone calls and texts were used for outreach in this study, but phone calls proved significantly more effective for recruitment with higher response rates (26.4% vs. 6.4%, P<0.001) and interest rates (11.4% vs. 1.8%) than texts.

Certain mHealth interventions incorporated culturally relevant content and linguistically accessible tools, such as Spanish-language apps and text messaging services, to maximize engagement. Both Arana-Chicas and Vaca demonstrated how addressing language barriers and logistical obstacles can improve engagement. Suffoletto’s studies highlight the ED visit as a location to identify young adults with hazardous drinking behaviors and intervene with theory-based messaging and self-monitoring reducing binge drinking. Other tools, including AB-CASI and A-CHESS, showed promise for alcohol and opioid recovery in specific subgroups. Collectively, these findings underscore the potential of culturally sensitive, theory-driven interventions for underserved populations disproportionately affected by substance use and health inequities.

Chronic disease and medication adherence (n=6 studies)

Patients with limited access to care or poor health literacy often rely on the ED for management of chronic diseases. This setting poses challenges, as providers rarely have time for comprehensive education or the capacity for follow-up to support adherence. However, ED-initiated mHealth interventions may help bridge this gap by offering automated reminders and consistent engagement to improve adherence and communication.

Specific to cardiovascular health, several studies evaluated digital interventions for hypertension management. Buis et al. evaluated a culturally tailored mobile intervention for blood pressure (MI-BP) aimed at improving blood pressure self-monitoring, sodium intake, physical activity, and medication adherence in 162 Black adults with uncontrolled hypertension (>135 mmHg) in Detroit, Michigan (39). After one year, the MI-BP group receiving a blood pressure cuff and educational materials showed significant decreases in average systolic blood pressure (sBP) adjusted for age and sex (22.5 mmHg decrease in MI-BP; P<0.001). In addition to these findings, Skolarus et al.’s Reach Out trial, drawn upon the Social Cognitive Theory, randomized 833 patients with severe hypertension in a safety-net ED to various combinations of behavioral text messaging, blood pressure monitoring prompts, and facilitated primary care appointments (scheduling and transportation) (40). All intervention arms reduced systolic BP by 6.6 mmHg over 12 months (95% CI: −9.3 to −3.8), suggesting the impact any structured intervention can make.

Beyond hypertension, digital message interventions have also been evaluated for the management of other chronic comorbidities. For diabetes care, Arora et al. performed a RCT of TExT-MED rooted in the Health Belief Model. This was a scalable, unidirectional, text-based intervention consisting of two daily messages for six months in English or Spanish, implemented in a public, urban ED with 128 adult patients with poorly controlled diabetes (41). While TExT-MED did not significantly improve glycosylated hemoglobin (HbA1c) levels generally between intervention and control (−1.05% vs. −0.60%; 95% CI: −0.27 to 1.17), it did lead to significant improvements in medication adherence. Furthermore, it had an extensive impact on Spanish-speaking participants who saw a meaningful increase in both medication adherence (1.1 vs. −0.3 on the Morisky Medication Adherence Scale) (95% CI: 0.2–2.7) and HbA1c reduction (−1.2% vs. −0.4%). For asthma control, Coker et al. created Text2Breathe, a text-message based intervention coupled with in-person health communication designed for parents of Medicaid-insured children with asthma who visited two urban pediatric EDs in the Pacific Northwest (42). This program included a 3-month interactive text program, including follow-up reminders, influenza vaccinations and additional educational content utilizing the Social Cognitive Theory. Although Text2Breathe did not lead to differences in asthma morbidity, it did result in a 35% higher average annual rate of primary care provider visits for asthma compared to the control [incidence risk ratio (IRR) =1.35; 95% CI: 1.03–1.76; P=0.03]. In addition, Stukus et al. showed through their Gamification Theory-based-based mobile application, AsthmaCare, that providing medication reminders, prompts on avoiding asthma triggers, and personalized electronic treatment plans can lead to participants being more responsible for their own care, and more likely to report improvement in asthma management after 6-month follow-up (79% vs. 64%, P=0.06) (43).

It is important to acknowledge that much of the impact of a digital intervention can be reliant on baseline participant characteristics, as shown in Wade et al.’s Self-Management After Recent Traumatic Brain Injury (SMART) trial, a RCT of adolescents aged 11–18 years with mild traumatic brain injury (mTBI) in the ED that were randomized into the SMART group (self-guided, online-based intervention) vs. usual care. The study showed that the effectiveness of the SMART app varies based on preinjury coping styles, suggesting there is a need for individual motivation by participants for an intervention to be successful (44). These studies suggest text messages and digital applications may improve symptom self-management and facilitate long-term adherence to care plans, albeit without definitive data on mHealth interventions leading to a reduction of future ED visits for chronic disease processes.

Linkage to care (n=5)

Patients discharged from the ED often require appropriate follow-up for ongoing chronic disease management or specialist evaluation. Adherence with follow-up appointments may improve health outcomes, reduce return visits to the ED, and overall reduce overreliance on the ED for further primary care (45,46). While a broad array of socioeconomic factors limits follow-up adherence, several easily addressable, commonly reported reasons included misunderstanding regarding follow-up date, time or simply that the appointment was forgotten.

Building on this idea, another study by Arora et al., they found that personalized, language-concordant text reminders significantly improved follow-up appointment attendance after ED discharge among low-income Hispanic patients in a per-protocol analysis (72.6% vs. 62.1%, P=0.045) (47). Bauer et al. demonstrated that a hybrid automated phone and SMS self-scheduling system significantly improved follow-up appointment adherence (49.3% vs. 23.4%, P<0.001) among underserved ED patients (48). However, there was no difference in ED revisits between the two groups within 120 days post-ED, consistent with other studies demonstrating that improvements in outpatient follow-up did not necessarily reduce reliance on the ED as a primary care setting (45,49). In contrary, in a blinded RCT at a pediatric trauma center in Florida, Salinero et al. found that a single text reminder did not improve follow-up adherence among pediatric ED patients (26% vs. 31%, P=0.69), though follow-up rates were higher for younger, higher-acuity patients and weekend visits (50).

Another cluster of studies focus on bidirectional text messages to improve follow-up outcomes. Obr et al. evaluated the effect of text-messaging as a mode of follow-up communication compared to usual care (typically no dedicated communication) on patient satisfaction (51). Text messaging showed no improvement in follow-up care or communication satisfaction in the intention-to-treat analysis. However, it significantly enhanced satisfaction among patients who engaged with the messages in the per-protocol analysis, with the study limited by the number of patients who completed surveys. Similarly, Bressman et al. found that a 30-day bidirectional text check-in system for high-risk, recently discharged patients did not reduce ED visits or readmissions (52). Despite high patient engagement, there was no significant reduction in acute care revisits compared to the control group within 30 days of discharge. While the intervention attempted to identify and address patient concerns after discharge, findings suggest the communication was insufficient to change patients’ trajectory.

Sexual health (n=2 studies)

Adolescents and young adults experience significant barriers to comprehensive sexual and reproductive healthcare due to limited education, confidentiality concerns, stigma, and restricted access, thus increasing the likelihood of pregnancy and the acquisition of sexually transmitted infections (STIs) (53). Young people aged 15–24 comprise of 13% of the U.S. population and approximately 25% of the sexually active population yet comprise nearly half of annual reported cases of gonorrhea, syphilis and chlamydia (54). Though pregnancy during adolescence is overall declining (55), rates remain disproportionately elevated in low-income, racial and sexual minority groups (56). Yet, adolescents and young adults who use the ED for care report sex with contraceptives placing them in need of interventions to meet their unmet sexual health needs (57,58). Therefore, leveraging mHealth interventions may bridge the gap between acute care and follow-up. While several pilot studies focused on improving adolescent and young adult sexual health using technology to increase contraceptive and condom use, few were powered to show efficiency or effectiveness. However, two notable studies offer insight. A RCT by Wolff et al. among 95 adolescent females with pelvic inflammatory disease (PID) in an urban pediatric ED found that daily text reminders significantly increased 72-hour follow-up rates to the ED compared to standard instructions [relative risk (RR) =2.9; 95% CI: 1.4–5.7] (59). Similarly, Reed et al. conducted a randomized trial of 584 adolescents with STIs, testing six result notification methods (60). While texting alone or the type of info card did not impact outcomes, combining phone calls and texts improved female notification rates (OR =3.2; 95% CI: 1.4–6.9), as did documenting a confidential phone number (OR =3.6; 95% CI: 1.7–7.5).

Safety and injury prevention (n=2 studies)

The final two RCTs by Suffoletto et al. present a theme in safety and injury prevention specifically evaluating the use of an automated, interactive, text-based intervention to promote safe vehicle behaviors based on Self-Regulation theory and Planned Behavior Theories. These programs are rooted in models of self-regulation, goal setting, planned behavior and health belief. One study tested the effectiveness of a text-based intervention to help promote seat belt use among young adults. A 6-week automated behavioral text program called Safe Vehicle Engagement (SaVE), implemented across four Pennsylvania EDs, aimed to increase seat belt use in 218 high-risk young adult drivers and passengers who reported irregular use in the past 2 weeks (61). At 6-week follow-up, SaVE, which included assessment with weekly feedback and goal setting, showed effectiveness in promoting short-term seat belt use (OR =2.8; 95% CI: 1.4–5.8; P=0.005), though this effect was not sustained at 12 weeks. There was no significant difference between intervention and control groups for always wearing seat belts at 12-weeks.

Expanding on this model, the same team adapted the SaVE intervention to address texting while driving (TWD) behaviors in a sample of 112 young adults recruited from four Pennsylvania EDs, all of whom reported recent TWD (62). If during the trial, participants reported no TWD, then they were sent goal reminders and positive reinforcement prompts. The 6-week program included weekly check-ins, feedback and goal setting, leading to a significant 71% odds reduction in self-reported TWD at 12 weeks compared to the control receiving no feedback (adjusted OR =0.29; 95% CI: 0.11–0.80). The two studies together show the effectiveness of automated text-based interventions for promoting road safety in at-risk youth during a critical life period. SMS interventions grounded in behavioral theory reduced TWD and increased seatbelt use.

Limitations

While mHealth is a growing and promising tool to be incorporated into the ED, limitations arose when evaluating the literature. Many of the studies dealt with short follow-up periods, limited between 3 to 6 months follow-up, which restricted assessment of long-term behavioral change. The exclusive inclusion of quantitative RCTs excluded qualitative perspectives on criteria such as patient experience, cultural acceptability, usability, feasibility and provider-centered insight on operational ED workflows and system integration. Furthermore, the external validity is limited as most patients were recruited by academic medical centers, large urban hospitals, or within one city, hindering generalization to broader populations. Culturally tailored interventions offered only bilingual support. Additionally, the review we conducted was unblinded which may introduce reviewer bias. While we recognize the importance of bias, as part of our study, we did not grade bias. In our perspective, grading bias is generally not a central component of narrative reviews whose scope is to provide an overview of evidence rather than critically appraise its quality. We also did not study the implementation of these interventions in the ED. Given that the chosen studies were RCTs, therefore assessing the efficacy of interventions, more information is needed to assess how best to implement digital health interventions into the complex acute care setting.

Future directions

Of 21 included studies, text messages and app-based interventions were the most common, making up 86% of the interventions. Text-based programs are highlighted for being simple, low-cost and largely accessible to study populations showing moderate short-term change in reducing risky behaviors, supporting chronic disease management and improving follow-up (Table 2). Additionally, 10 of 21 studies explicitly discussed the theories by which interventions were built upon. Of the 10 behavioral studies, the majority were interventions that administered text messages and incorporated theories such as the Social Cognitive Theory, the Health Belief Model and Theory of Reasoned Action. These principles were key components in studies that demonstrated utility in high-risk ED populations, promoting behavioral change and chronic disease management (Table 2). For example, the SaVE interactive text intervention which led to a 71% odds reduction in self-reported TWD was framed upon goal setting, the Health Belief Model, Self-Regulation Theory and the Theory of Planned Behavior. Ultimately, the literature suggests that low-cost, bidirectional programs rooted in behavioral frameworks and cultural competency represent the most promising strategies for driving behavior change and improving care continuity for high-risk ED patients.

mHealth tools can be leveraged to remedy structural inequities by increasing access and addressing barriers associated with care. Recent work by Glynn et al. supports the feasibility of digital tools in the ED, noting that smartphone access is ubiquitous even among marginalized populations. However, feasibility does not equate acceptability as patients had expressed hesitations in sharing passive data to third party apps, citing privacy concerns and medical mistrust (63). For mHealth interventions that address sensitive topics such as substance use, interventions must prioritize ethical design, transparency and secure metadata to build trust and promote use. As healthcare becomes increasingly digitized, particularly with the evolution of artificial intelligence, mHealth interventions are likely to become more advanced, powerful, and even more individualized. Future mHealth interventions should expand on cultural adaptability with multilingual platforms.

Further, integrating mHealth into ED workflows increases efficiency and optimizes resources in an over-burdened, over-crowded setting triaged by acuity. mHealth in the ED may also streamline post-discharge care, improve follow-up, and ultimately reduce the burden on acute care settings. Longer-term studies are warranted to assess lasting changes in patient outcomes as well as ED visit utilization.

Conclusions

This review highlights the growing role of mHealth in the ED as a feasible, adaptable, and low-cost strategy to address diverse health needs. Digital tools, particularly text messaging and mobile applications, have shown moderate short-term benefits in reducing risky behaviors, improving follow-up, and supporting chronic disease management. Many interventions targeted populations who disproportionately rely on the ED, underscoring the potential of mHealth to advance health equity.

Most evidence comes from short-duration trials in academic urban centers, with limited data on long-term outcomes, cost-effectiveness, or real-world integration into ED workflows. Sustained benefit will likely require interventions that are theory-driven, culturally and linguistically tailored, and embedded into routine ED care. Future work should also assess implementation strategies, scalability, and equity impacts to ensure that digital innovations reduce disparities rather than widen them.

In sum, mHealth interventions in the ED show promise as tools to extend preventive care, strengthen continuity, and alleviate system pressures. Realizing their potential will require moving beyond proof-of-concept trials toward pragmatic studies that evaluate durability, implementation, and impact across diverse patient populations.

Acknowledgments

We would like to thank Dr. Le Minh Giang for the advisement of this paper.

Footnote

Reporting Checklist: We present this article in accordance with the Narrative Review reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-63/rc

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-63/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-63/coif). J.Z. reports the grants from NIAID at the National Institutes of Health, Centers for Disease Control and Prevention. The author also reports honoraria for academic presentations from academic medical centers for Grand Rounds, IAS-USA, AETC, AMA, and serving on the IAS-USA Scientific Leadership Board. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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