Digital Duality: Exploring Mobile Technology's Impact on Healthcare Professionals’ Well-being and Burnout

2.1. Mobile Health

The use of technology by healthcare workers has increased dramatically with EHR deployment in hospitals growing from 10% in 2008 to 96% in 2015 (Henry et al., 2016) and similar trends have been observed in other healthcare settings (Dougherty et al., 2023). Research consistently highlights multiple challenges, including inefficient user interfaces (Babbott et al., 2014; Kroth et al., 2018), increased documentation time compared with paper records (Poissant et al., 2005), information overload during encounters (Feblowitz et al., 2011), greater clerical burden (Shanafelt et al., 2016), and negative effects on physician–patient communication (Pelland et al., 2017; Toll, 2012), all of which may contribute to burnout. During the same period, telemedicine utilization has increased significantly (Galewitz, 2020; Kokalitcheva, 2016) with the COVID-19 pandemic further accelerating the adoption of virtual care (Yellowlees et al., 2020). Although telemedicine expands access, studies note that it requires increased concentration (Ferran & Watts, 2008; Kadaba, 2020), can be more taxing than face-to-face encounters (Fosslien & Duffy, 2020; Kadaba, 2020), and may itself contribute to burnout (Nakagawa & Yellowlees, 2020).

mHealth is one of the fastest growing sectors within electronic health (eHealth) (Free et al., 2013) and is the application of mobile telecommunication technology to deliver healthcare, (Istepanian et al., 2003) which is accessible to healthcare providers and patients via smartphones, laptops, personal digital assistants (PDAs), tablets, and also includes wearables which allow patients to share data with providers (Akter et al., 2013; Akter & Ray, 2010; Vital Wave Consulting, 2009). Evidence indicates that mHealth has the potential to reduce cost and overcome barriers of time and geography (Gurchiek et al., 2019; Naslund et al., 2016; Tachakra et al., 2003), improve workflows (Ventola, 2014), enhance patient–provider communication (Krohn, 2015; C. Lu et al., 2018), support clinician well-being (Chen et al., 2018; Fortney et al., 2013; Foureur et al., 2013; Krasner et al., 2009; Pospos et al., 2018; Sanko et al., 2016), and even allow monitoring of burnout risks (Chen et al., 2018). Although mHealth technology has great potential, it also has several weaknesses including unreliable connectivity, poor battery life, the need to enter passwords, and the size of the screen for smartphones and PDAs (Farrell, 2016; Power et al., 2014; Webb et al., 2016).

2.2. Burnout

Burnout refers to “a psychological syndrome of emotional exhaustion, depersonalization, and reduced personal accomplishment, which can occur among individuals who work with other people in some capacity” (Maslach, 1993, p. 20). In understanding the components of burnout, previous research identified the three main dimensions: (1) exhaustion—profound physical, cognitive, and emotional fatigue that undermines people's ability to work effectively and feel positive about what they’re doing; (2) cynicism—refers to an erosion of engagement, or a distancing of one's self psychologically from their work; and (3) inefficacy—feelings of incompetence and lack of productivity, typically resulting from the first two dimensions of burnout (Koutsimani et al., 2019).

Occupational stress leads to burnout and the rate of burnout among healthcare providers is particularly high (McCray et al., 2008; Rotenstein et al., 2018; Shanafelt et al., 2012; Willcock et al., 2004). Burnout has well-documented negative effects on clinicians, including detrimental impacts on mental health and work engagement (Clough et al., 2019; Zaghini et al., 2020). The COVID-19 pandemic further intensified these risks, leading to increases in stress and burnout worldwide (W. Lu et al., 2020; Neto et al., 2020; Pappa et al., 2020). In the U.S., burnout also carries significant organizational costs, such as financial strain (Magtibay et al., 2017), medical errors, lower quality of care, and poorer patient outcomes (Yates, 2020).

Maslach et al. (2001) suggests that work experience is confounded by age and for nurses, being younger and with less experience is associated with higher risk of burnout (Gauthier et al., 2015) while for physicians, studies suggest that older generations have higher anxiety associated with the adoption and use of technology which may contribute to burnout (Nakagawa & Yellowlees, 2020; Olson et al., 2011; Van Volkom et al., 2014). Improved communication has the potential to reduce burnout (Dijxhoorn et al., 2021; G. Martin et al., 2019) and mHealth has the potential to facilitate communication and teamwork if healthcare organizations can overcome the technological, security, and organizational challenges associated with effectively deploying these applications (S. K. Martin et al., 2017).

2.3. mHealth and Burnout

While mHealth is a broad construct, when overlapping with the topic of burnout, research has predominantly been on psychological applications such as meditation and mindfulness. Most studies observed significant reductions in burnout symptoms, particularly in emotional exhaustion (Bhardwaj et al., 2023; Boucher et al., 2023; Ito-Masui et al., 2023; Lambert et al., 2020; Mistretta et al., 2018; Monfries et al., 2023). though some reported no substantial effects (Ditton et al., 2023; Fiol-DeRoque et al., 2021; Taylor et al., 2022). Across studies, the effects of mHealth on burnout dimensions are mixed. Two studies demonstrated improvement in only emotional exhaustion (Mistretta et al., 2018; Monfries et al., 2023), while two other studies found improvements in both emotional exhaustion and cynicism (depersonalization) (Boucher et al., 2023; Ito-Masui et al., 2023).One study identified gains in emotional exhaustion and inefficacy (feelings of incompetence) (Lambert et al., 2020), and another demonstrated benefits across all three components (Bhardwaj et al., 2023). In contrast, three studies demonstrated no improvement in any of the components of burnout (Ditton et al., 2023; Fiol-DeRoque et al., 2021; Taylor et al., 2022), underscoring the variability of outcomes in literature.

Further exploration reveals that application design and organizational characteristics are pivotal in deploying and adopting these technologies. Positive factors associated with adoption include sufficient training (Sorin et al., 2023), adequate workflow integration (Stoumpos et al., 2023), favorable organizational structures (Patel et al., 2021), user-friendly design (Haleem et al., 2021). On the other hand negative factors include unfavorable social structures (Gordon et al., 2020), lack of training (Patel et al., 2021), lack of tailored tool design (Mirbabaie et al., 2022), and insufficient design of user interface (Mirbabaie et al., 2022).

Complementing the empirical research, we identified theoretical or conceptual papers that discuss the potential of mHealth technologies (Paranjape et al., 2020), suggest frameworks for their evaluation (Lanham et al., 2020; Nakagawa & Yellowlees, 2019; Tauro et al., 2022), describe mHealth technology with the potential to aid in the delivery of healthcare (Fonseca et al., 2023; Miner et al., 2019), describe experimental design to evaluate mHealth applications (Serrano-Ripoll et al., 2021; Wiederhold et al., 2016), solicit feedback from potential users regarding proposed design of mHealth applications (Adler et al., 2022; Haddad et al., 2022), outline a professional society's proposed position on development and use of mHealth applications (Meder et al., 2023), and make policy recommendations for development and use of digital health technologies (O'Reilly-Jacob et al., 2021; Ommaya et al., 2018).

This landscape of mHealth in healthcare sets the stage for a deeper exploration of how these technologies interact with various elements of the work environment. The complex interplay between the inherent demands and resources offered by mHealth technologies suggests a nuanced framework is needed to fully understand and leverage these tools in combating burnout.

2.4. Theoretical Background

The Job Demands-Resources (JD-R) model provides a useful framework for understanding the complex dynamics of burnout among healthcare professionals. Developed by Demerouti et al., this model posits that every occupation has certain job demands and resources that can influence employees’ stress levels and, consequently, their risk of burnout. The JD-R model is a validated theoretical framework for understanding workplace burnout and its antecedents (Bakker & Demerouti, 2007). According to the JD-R model, job demands refer to those physical, psychological, social, or organizational aspects of a job that necessitate sustained physical and/or psychological effort, often associated with physiological and psychological costs (Demerouti et al., 2001). Conversely, job resources are the aspects of a job that can help reduce job demands and their associated physiological and psychological costs, contribute to achieving work goals, and stimulate personal growth, learning, and development (Bakker & Demerouti, 2007).

As previously mentioned, burnout is a response to prolonged exposure to physical or emotional stressors which lead to feelings of exhaustion, cynicism, and ineffectiveness or lack of accomplishment (Maslach & Leiter, 2005, p. 2016). Prior studies found that job demands are primarily related to exhaustion, while job resources are primarily related to cynicism (Demerouti et al., 2001) and professional efficacy (Bakker et al., 2003; Demerouti et al., 2001). Specifically, according to Bakker and colleagues (2003) elevated job demands primarily lead to increased exhaustion among employees, while cynicism and professional efficacy are less directly impacted, only experiencing effects through the lens of exhaustion. Conversely, a scarcity of job resources is associated with heightened cynicism and diminished efficacy, without directly influencing exhaustion levels. In scenarios characterized by both high job demands and insufficient job resources, employees are likely to experience exhaustion, cynicism, and a diminished sense of competence, collectively manifesting as burnout. Broadly, the workplace appears to foster two principal dynamics. The first is a health impairment dynamic, originating from job demands and culminating in exhaustion. The second dynamic is motivational, propelled by the presence of job resources and associated with feelings of competence. The absence of sufficient resources leads to increased job cynicism and lower efficacy levels.

Historically, job demands and resources were viewed as relatively static elements, closely tied to the physical work environment, job design, and organizational policies (Bakker & Demerouti, 2007; Demerouti et al., 2001). Within the healthcare sector, job demands often encompass long working hours, the emotional weight of patient care, the pressure of making high-stakes decisions, and the requirement for meticulous attention to detail. Conversely, job resources can manifest as supportive colleagues, a positive team culture, effective communication tools, opportunities for professional growth, and streamlined workflow systems. However, the advent of rapid digitalization and the widespread integration of mobile technology into the workplace necessitate a reevaluation and expansion of this model to account for new dynamics. Mobile technology uniquely identifies as being a job demand and a resource. Its role varies based on its application and management within the work setting. Examples of mobile technology serving as a job demand include the expectation of constant availability leading to longer work hours and increased stress. As a resource, mobile technology can enhance accessibility to information, improve communication efficiency, and offer platforms for online training and development, thereby facilitating better work-life balance and supporting continuous professional development. This nuanced understanding underscores the importance of strategically managing digital tools to maximize their benefits while minimizing potential stressors, thereby adapting to the evolving landscape of workplace demands and resources.

2.5. Conceptual Framework

In the proposed conceptual framework illustrated in Figure 1, we explore the multifaceted role of digital transformation in healthcare environments, drawing upon the foundational principles of the Job Demands-Resources (JD-R) model. This model serves as a guiding framework for our exploration, allowing us to systematically examine how digitalization impacts the balance of job demands and resources, and subsequently, the psychological well-being of healthcare professionals. By integrating the JD-R model into our analysis, we identify and articulate two pivotal processes: the health impairment process, instigated by increased job demands, and the motivational process, driven by the provision of job resources. As digital technologies increasingly infiltrate healthcare practices, they introduce a complex spectrum of challenges and opportunities that critically influence the work environment and employee outcomes. Building on the foundation laid by the Job Demands-Resources (JD-R) model, our conceptual framework further explores the nuanced effects of digital transformation in healthcare through a series of propositions:

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Figure 1.

Conceptual framework. Source: Authors’ elaboration based on the Job Demands–Resources (JD-R) Model (Demerouti et al., 2001; Bakker & Demerouti, 2007).

Proposition 1: Digital Transformation Intensifies Job Demands. As healthcare systems increasingly adopt digital technologies, the complexity and volume of job demands surge. These demands include the need for constant technological proficiency, adaptation to digital workflows, and the management of digital information. Specifically, mHealth applications require healthcare professionals to remain proficient with a range of mobile platforms, from remote monitoring tools to mobile EHRs, adding layers of complexity to their daily routines and requiring healthcare professionals to manage 24/7 availability for urgent patient communications and real-time health data monitoring. This not only intensifies the frequency and complexity of interactions but also the need for immediate responsiveness, substantially increasing the risk of exhaustion. Prior research has shown that inefficient user interfaces, increased documentation burden, and clerical workload are major contributors to burnout (Babbott et al., 2014; Kroth et al., 2018; Poissant et al., 2005; Shanafelt et al., 2016), while studies of telemedicine similarly emphasize its cognitive demands (Ferran & Watts, 2008; Kadaba, 2020). This proposition addresses the empirical gap that, while EHR-related burdens have been widely studied, far less is known about how mHealth-specific features (e.g., constant notifications, asynchronous communication, device-based monitoring) shape burnout risks. By situating mHealth within the JD-R model, we extend prior research to highlight how mobile-specific demands introduce new pathways to exhaustion, a contribution that goes beyond the traditional focus on desktop or institutional technologies.

Proposition 2: Digital Transformation Bolsters Job Resources. Simultaneously, digital transformation presents a unique opportunity to enhance job resources. The introduction of digital tools and platforms can significantly improve communication, information accessibility, and workflow efficiency. mHealth platforms, for instance, facilitate streamlined communication channels that can support more efficient patient management, reducing unnecessary face-to-face consultations. These enhancements align with the JD-R model's depiction of job resources as essential elements that mitigate job demands, promote employee engagement, and foster a motivational process, thereby enhancing feelings of competence and reducing experiences of cynicism. This perspective is supported by research showing that mHealth improves workflows (Ventola, 2014), enhances patient–provider communication (Krohn, 2015; Lu et al., 2018), and supports clinician well-being (Chen et al., 2018; Fortney et al., 2013; Foureur et al., 2013). This proposition is important since it addresses a theoretical gap. Existing burnout research often emphasizes digital technology as a burden but under-theorizes its potential as a resource (Phi et al., 2025). By positioning mHealth as a dual actor, this proposition advances JD-R applications into digital health, demonstrating how mobile tools may actively buffer against cynicism and inefficiency when deployed under supportive conditions.

Proposition 3: High Job Demands Facilitate Exhaustion, Influencing Cynicism and Efficacy Indirectly. In accordance with the JD-R model, our framework posits that the increased job demands resulting from digital transformation directly lead to exhaustion. This exhaustion, in turn, indirectly affects other dimensions of burnout, such as cynicism and a reduced sense of professional efficacy. This proposition highlights the complex cascade effect where primary exposure to high demands sets off a chain reaction impacting all facets of burnout. This sequential process is consistent with prior JD-R applications, where job demands are primarily linked to exhaustion and indirectly to cynicism and efficacy (Bakker et al., 2003; Demerouti et al., 2001). In healthcare specifically, EHR burdens and constant digital adaptation have been identified as drivers of exhaustion (Shanafelt et al., 2016; Nakagawa & Yellowlees, 2020). This proposition deepens prior work by clarifying how mHealth-driven demands create indirect pathways of harm. While the JD-R model has been applied to other industries, empirical studies in healthcare have rarely examined the sequential relationship between digital demands, exhaustion, and downstream outcomes like cynicism or efficacy. By exploring this mediating process, the proposition fills a theoretical gap and suggests a testable model for future empirical studies.

Proposition 4: Insufficiency of Job Resources Directly Elevates Cynicism and Lowers Efficacy. Reflecting the JD-R model's insights, a lack of adequate job resources in a digitally transformed healthcare environment, such as insufficient training on new mHealth tools or poor integration of these technologies into daily practices, directly leads to increased cynicism and diminished feelings of efficacy. Without sufficient support, tools for efficiency, and opportunities for professional development, healthcare professionals may experience a significant decline in motivation and engagement. This claim is reinforced by evidence that organizational structures, workflow integration, and user-friendly design facilitate adoption (Patel et al., 2021; Sorin et al., 2023; Stoumpos et al., 2023; Haleem et al., 2021), whereas lack of training, poor design, and unfavorable social structures undermine adoption and increase stress (Mirbabaie et al., 2022; Gordon et al., 2020). This proposition addresses the gap that, while organizational support and training are widely recognized as moderators of technology adoption (Husain et al., 2024), they have rarely explicitly theorized as “resources” within the burnout literature. By reframing these elements as core resources in the JD-R model, this proposition advances existing knowledge by linking organizational-level interventions directly to psychological outcomes.

Proposition 5: The Intersection of High Demands and Low Resources Culminates in Burnout. This proposition underscores a critical juncture within the JD-R framework, where the simultaneous presence of high job demands and insufficient job resources precipitates the full spectrum of burnout, characterized by exhaustion, cynicism, and reduced efficacy. This scenario epitomizes the adverse outcomes the JD-R model seeks to address, highlighting the imperative for balanced digital transformation strategies that both manage demands and bolster resources. This dynamic is consistent with Maslach and Leiter's (2005, 2016) work showing that prolonged exposure to stress without adequate resources leads to exhaustion, cynicism, and inefficacy, and with healthcare studies emphasizing that insufficient organizational support during digital adoption exacerbates burnout (Martin et al., 2017; Nakagawa & Yellowlees, 2020). While this dynamic is central to the JD-R model, it has not yet been systematically applied to the context of mHealth. This proposition advances knowledge by explicitly theorizing the “tipping point” at which mobile technologies shift from supportive tools to burnout accelerators. By situating mHealth adoption within this intersection, we identify a boundary condition that future empirical research can measure and test, thereby grounding the framework in observable organizational realities.

As Figure 1 shows, digitalization is shown on the left as a driver that simultaneously generates new job demands and job resources. Job demands (e.g., constant connectivity, real-time monitoring, increased documentation) flow into the health impairment pathway, primarily resulting in exhaustion (P1, P3). In contrast, job resources (e.g., improved communication channels, access to patient data, workflow efficiency) support the motivational pathway, fostering efficiency and reducing cynicism (P2, P4). The intersection of high demands and insufficient resources leads to the full spectrum of burnout (P5).

By explaining these dynamics, the framework offers valuable insights into the pathways through which digitalization influences healthcare professionals’ well-being and presents opportunities for mitigating burnout through strategic interventions. This comprehensive approach underscores the importance of a nuanced understanding of job demands and resources in the digital age, aiming to support healthcare organizations in fostering a supportive and sustainable working environment for their employees.

3.1. Theoretical Implications

Our study contributes significantly to the existing body of knowledge by applying the Job Demands-Resources (JD-R) model within the context of mHealth. We offer a novel conceptualization of mobile technology as both a job demand and a job resource, which deepens the understanding of its dualistic impact on healthcare workers’ psychological outcomes. This approach facilitates a more nuanced analysis of how specific features of mHealth applications can differentially influence the components of burnout: exhaustion, cynicism, and inefficacy.

Firstly, by identifying mHealth as a job demand, we align with previous research suggesting that increased technological demands can lead to exhaustion due to constant connectivity and the resultant information overload (Bhardwaj et al., 2023; Lambert et al., 2020). However, by also framing mHealth as a resource, we illuminate its capacity to mitigate burnout by providing support and efficient communication tools that can reduce feelings of inefficacy and cynicism among staff (Mistretta et al., 2018; Monfries et al., 2023).

This dual-factor approach not only extends the JD-R model, but also invites further theoretical exploration into the specific conditions under which technology shifts between being a demand and a resource. For instance, the features of mHealth that are perceived as demands in one setting may be viewed as resources in another, depending on organizational support systems and individual user characteristics (Mirbabaie et al., 2022; Patel et al., 2021).

3.2. Practical Implications

From a practical standpoint, our findings offer actionable insights for healthcare administrators and policymakers focused on optimizing the use of mHealth in clinical settings. To leverage mHealth as a positive force against burnout, it is crucial to balance its demands with sufficient resources. This balance begins with the implementation of mHealth tools that are user-friendly and designed to simplify rather than complicate daily tasks (Haleem et al., 2021; Mirbabaie et al., 2022). By selecting intuitive tools that align with the natural workflows of healthcare staff, administrators can significantly reduce the cognitive load and enhance user satisfaction.

In addition to intuitive design, comprehensive training programs are essential (Sorin et al., 2023). Such programs should not only teach healthcare professionals how to use mHealth applications but also highlight how these tools can streamline their work and improve outcomes. By enhancing digital literacy, training can reduce the perception of technology as a complex demand, transforming it into a supportive resource that staff feel competent and comfortable using (Stoumpos et al., 2023).

Moreover, healthcare organizations should consider regular assessments of how technology impacts staff. This involves gathering feedback through surveys, interviews, and direct observation of how mHealth tools are used in the clinical environment (Adler et al., 2022; Haddad et al., 2022). By understanding these impacts, administrators can make informed decisions about which technologies to adopt and how to implement them, ensuring that these tools truly meet the needs of their staff without adding unnecessary burden.

It is also crucial for healthcare managers to customize mHealth tools to match specific workflows within different departments. Personalization of technology can help ensure that mHealth applications provide relevant and timely support to all healthcare professionals (Mirbabaie et al., 2022), regardless of their specialty. This customization not only enhances efficiency but also prevents the feeling of technology overload, thereby reducing stress and potential burnout.

Finally, and perhaps most importantly, fostering a culture of digital wellness is vital. This aspect of the practical implications cannot be overstressed. Healthcare managers should actively develop and enforce policies that promote balanced technology use. Establishing clear boundaries for work-related communications and creating spaces for digital disconnection are essential strategies (O'Reilly-Jacob et al., 2021). Such measures not only help in maintaining a healthy work-life balance but are critical in mitigating the risk of digital burnout. Encouraging practices that support digital mindfulness—where staff are educated on the benefits of periodically unplugging from technology—can further enhance this culture. This commitment to digital wellness helps sustain the long-term use of mHealth tools in a way that supports the health and productivity of healthcare professionals, rather than undermining it.

Importantly, these strategies should be viewed within a broader post-COVID digital transformation trajectory. While the pandemic accelerated rapid adoption of digital tools, the ongoing challenge is to move beyond short-term crisis responses and embed mHealth into sustainable, long-term organizational practices. Positioning the framework in this wider context underscores that the relevance of mHealth is not limited to pandemic-era adaptations but is central to the future of healthcare delivery, workforce well-being, and system resilience.

Beyond organizational strategies, the proposed framework can also guide future empirical research. For example, researchers could operationalize its constructs by developing survey instruments or observational protocols that measure both the “demand” and “resource” aspects of mHealth in clinical settings. Longitudinal or mixed-methods studies could test how changes in these measures over time influence burnout outcomes, allowing the framework to be validated and refined. In doing so, empirical research would not only ground the framework in real-world evidence but also generate actionable insights for administrators on tailoring technology adoption to maximize staff well-being.

By prioritizing these strategies, healthcare administrators can ensure that mHealth technologies serve as a beneficial force in the clinical environment, enhancing the well-being and productivity of healthcare professionals while mitigating the risks of burnout.

3.3. Limitations and Future Research

Our study presents some limitations, suggesting avenues for future research. One limitation is the conceptual nature of our approach, relying heavily on theoretical frameworks and existing literature rather than empirical data. As such, the actual impact of mHealth on burnout needs to be explored through quantitative and qualitative studies that can provide empirical evidence to support or refute our propositions.

Future research should also consider the heterogeneity of healthcare settings and cultural differences in technology adoption and burnout perceptions. Studies could examine how different types of mHealth applications affect various healthcare roles, potentially tailoring applications to meet the unique demands of different specialties. Additionally, longitudinal studies are needed to assess the long-term effects of mHealth on burnout, which could inform more sustainable technology management strategies in healthcare.