The study was informed by a pragmatic research paradigm, integrating theory-driven (UTAUT2-based) and data-driven approaches to understand real-world user experiences and implementation processes. A convergent mixed methods design was used, in which qualitative and quantitative data were collected in parallel, analyzed separately, and subsequently integrated at the interpretation stage to provide a comprehensive understanding of participants’ experiences and acceptance factors [25].

The study was conducted in Pyeongchang County in South Korea. The county is characterized by geographic dispersion with limited public transportation infrastructure. Residents must travel 40‐80 kilometers to access hospital services. At the time of this study, 4 major mHealth services were implemented across Gangwon State (Table 1). Each service leverages a mobile platform and is connected to biometric devices to support chronic disease management. Three services, Gangwon Health Up, Value Health, and AI/IoT Senior Health Management Service, primarily target adults and older adults diagnosed with hypertension or diabetes, whereas the mobile health care service focuses on adults with risk factors for metabolic syndrome. Smartphone apps, Bluetooth-enabled devices, and algorithm-based feedback systems provide support in tracking blood pressure, blood glucose, physical activity, dietary behaviors, and other health indicators. Periodic assessments by health care staff, mostly nurses, are conducted through public health centers using digital reporting systems. Together, these 4 interconnected services constitute Gangwon State’s digital health ecosystem, offering technology-assisted self-management, individualized monitoring, and coordinated public health support for chronic disease prevention and control.

Purposive sampling was used to recruit participants through health care staff at public health centers. Inclusion criteria were as follows: (1) at least 6 months of experience using mHealth services, (2) ability to communicate in Korean, and (3) willingness to provide informed consent. Participants were excluded if they used the digital health application less than one-third of weekdays (fewer than 6 days per month), as this level of engagement was considered insufficient to provide meaningful insights into their experiences with the mHealth services.

This study was approved by the Institutional Review Board of Hallym University (HIRB-2025‐129). All participants provided written informed consent prior to participation. To ensure privacy and confidentiality, all participant names were removed from the transcripts and responses and replaced with sequential identification numbers. All data were stored securely and accessible only to the research team. As a token of appreciation, participants were provided with gift certificates.

Characteristics of the study participants are presented in Table 2. The study included 24 participants, with 6 individuals enrolled in each mHealth service. The mean age was 71.3 (SD 9.2) years, and most participants were female (17/24, 70.8%). Participants commonly had multiple chronic conditions, with hypertension being the most prevalent (18/24, 75%), followed by hyperlipidemia (14/24, 58.3%), diabetes (10/24, 41.7%), and osteoarthritis (6/24, 25%). In terms of service engagement, 33.3% (8/24) of participants had used the services for 6‐12 months, 41.7% (10/24) for 1‐3 years, and 25% (6/24) for more than 3 years.

Quantitative findings related to technology use are summarized in Table 3. Participants reported a low perceived difficulty score, with a mean of 2.54 (SD 2.06) on a 0‐10 scale, where higher scores indicate greater perceived difficulty. This suggests that, overall, participants perceived the mHealth services as relatively easy to use.

Most participants expressed strong behavioral intention toward continued use. Specifically, 95.8% (23/24) indicated that they intended to continue using the services long term, while only 1 participant reported otherwise. Recommendation intention was uniformly high, with all participants indicating that they would recommend the service to others.

Willingness to pay for continued use was less consistent compared to other indicators. While the majority of participants (19/24, 79.2%) reported willingness to pay, a subset (5/24, 20.8%) indicated that they would prefer to use the service only if it remained free. Among those willing to pay, the most commonly reported acceptable monthly cost ranged between KRW 1000 and 5000 (US $1-3) (11/24, 45.9%), followed by KRW 6000‐10,000 (US $4-7) (6/24, 25%) and KRW 10,000‐20,000 (US $7-13) (2/24, 8.3%) (Table 4).

Across the UTAUT2 constructs, performance expectancy, social influence, facilitating conditions, and habit emerged as the most salient drivers of acceptance and continued use. Effort expectancy and hedonic motivation were present but less prominent, likely because sustained nurse support reduced usability barriers over time, and enjoyment emerged primarily after routines were established.

This study explored the experiences and acceptance of community-based mHealth services among residents in an underserved rural area in South Korea, revealing that these services were gradually integrated into participants’ everyday lives. Through regular monitoring, participants reported that they could clearly sense changes in their bodies, that they were no longer managing their health alone, and that using the devices had ultimately become a part of their daily routine. Nurses working in the public health centers provided continuous support, such as troubleshooting and accountable monitoring, which facilitated participants’ adoption of the device. Interpreted through the UTAUT2 framework, these experiences indicate that performance expectancy, social influence, facilitating conditions, and habit jointly reinforced behavioral intention and continued use of community mHealth services among the research participants.

Performance expectancy is a central predictor of technology adoption and continued use within the UTAUT2 framework [20]. The findings of this study further support this construct, as participants emphasized perceived health benefits as the primary motivation for engaging with mHealth services. Real-time visualization of vital signs and the results of physical activity allowed participants to link their daily behaviors to measurable health indicators. They described specific lifestyle modifications, such as reducing carbohydrate intake, increasing vegetable consumption, and maintaining step-count targets, which they attributed directly to feedback from the devices and apps. Some participants reported perceived prevention of disease progression. For example, they detected prediabetes earlier than they would have through routine clinic visits. These experiences align with prior mHealth research showing that digital self-monitoring has been associated with improvements in chronic disease management and prevention outcomes, such as blood pressure and glycemic control, especially when combined with coaching or tailored feedback [28-30]. Studies using the UTAUT2 framework in older adult and chronic illness populations have similarly reported that strong beliefs about health improvement are among the most powerful determinants of intention to use and actual usage behavior. In this study, these beliefs were reinforced as participants could literally “see” their improvements reflected in the app’s numbers and graphs, allowing them to connect these visualized changes to concrete behavioral adjustments [24].

Our findings extend this literature by illustrating how performance expectancy operates in a rural public health setting where clinic visits are infrequent and travel distances are long. For many participants, mHealth services effectively compressed the monitoring interval from every few months in a hospital to daily at home. This shift appears to have strengthened the perceived use of the technology, as participants came to view mHealth as a useful tool for maintaining stability in their chronic conditions between formal clinic visits [19,24].

Our findings highlight the pivotal role of nurses and social relationships in shaping both social influence and facilitating conditions. In this setting, community health center nurses delivered hands-on training, offered ongoing troubleshooting, interpreted health data, and coordinated group exercise and peer interactions. These human-in-the-loop functions enabled older adults and individuals with low digital literacy to overcome early usability barriers and develop trust in mHealth systems, consistent with prior evidence on nurse-led telehealth and digital chronic care support [2,3,5].

Nurses played dual roles within the UTAUT2 framework. They functioned as facilitating conditions by providing training, troubleshooting, and device maintenance, while simultaneously serving as sources of social influence through encouragement, accountability, and trust. This overlap highlights the limitations of rigid construct boundaries in team-based primary care contexts.

As group activities and peer comparison increased, social influence shifted from individual encouragement to a shared sense of “doing it together.” This collective engagement enhanced hedonic motivation [11,30]. Participants described enjoyment, friendly competition, and pride in meeting daily goals. Elements such as comparing step counts or receiving recognition during group sessions introduced a game-like dynamic that strengthened adherence. Although small incentives were appreciated, many participants emphasized intrinsic motivation (“staying healthy”) over external rewards. As a result, device use was not perceived as burdensome [17].

Overall, these findings, which are consistent with evaluations of mHealth use among middle-aged Korean adults in their 50s and 60s [24], suggest that the successful scale-up of digital health interventions in rural areas depends more on relationship-centered care models than on technology dissemination alone. At the study sites, nurses were the only health care professionals available to manage the mHealth services, as recruiting physical therapists, nutritionists, or other allied health professionals was particularly challenging in these rural settings. Because the study sites were public health centers, nurses naturally assumed human-in-the-loop roles, providing continuous monitoring, encouragement, and contextualized feedback to participants. However, as primary care increasingly emphasizes high-quality, team-based care, long-term strategies are needed to establish mobile and multidisciplinary primary care teams, rather than relying solely on nurses, in order to effectively support chronic disease management among rural residents [31].

Furthermore, implementation science research is needed to better understand how mHealth interventions are integrated into routine clinical practice, sustained over time, and adapted to the contextual constraints of underserved rural settings. In addition, economic factors, including program financing, workforce costs, and patients’ willingness to pay, remain relatively understudied and should be systematically examined to inform the scalability and sustainability of rural digital health initiatives.

Habit was a key construct shaping participants’ sustained engagement with mHealth services. Most participants reported that checking their blood pressure, reviewing their step counts, or looking at daily summaries had become automatic behaviors, often compared to brushing their teeth. These routines were anchored at specific times, on waking, after exercise, and before going to bed, and were reinforced by consistent cues such as device placement, app notifications, and scheduled group activities. This pattern aligns with recent technology acceptance research emphasizing that, beyond initial intention, habit and automaticity are critical for explaining long-term use of mHealth and wearable technologies among older adults and people with chronic diseases [24].

Designing interventions that incorporate clear goals, progress visualization, social reinforcement, and modest gamification may help accelerate the transition from conscious effort to self-sustaining habit. Importantly, the sources of enjoyment described here were not purely digital; they were relational and communal, arising from doing activities with peers and being acknowledged by nurses and staff.

Despite generally positive experiences, older participants and those with minimal prior digital exposure reported usability challenges. These participants often found initial setup confusing, required 6 months or longer to feel comfortable with the devices, and remained more dependent on nurses or family members for troubleshooting. Difficulties included small text, complex menus, unreliable Bluetooth connections, and anxiety about “breaking” the device through incorrect use. These findings echo previous findings that older adults in rural settings tended to have lower levels of education, high rates of frailty, and limited prior exposure to smartphones and apps constrain digital health adoption [32]. In terms of the reminder system, using color-based alerts or a 5-star rating method instead of text-based warning messages may need to be considered. Nevertheless, our findings demonstrate that these participants were able to develop confidence and proficiency over time when supported by consistent hands-on guidance from health care staff or coordinators. Although their adaptation period was longer, participants eventually became familiar with the devices and expressed a sense of empowerment in being able to use new technology, which in turn enabled them to experience the benefits of improved self-management and health outcomes. These results underscore the need for structured, hands-on guidance materials and training protocols tailored specifically for older adults in rural communities, ensuring that digital health innovations remain accessible and equitable for populations with lower digital readiness.

Although this study provides valuable insights into older adults’ experiences with mHealth for chronic disease prevention and management in rural Korea, several limitations should be noted. Participants were current users with at least 6 months of engagement, which may overrepresent positive experiences and undercapture reasons for nonadoption or discontinuation. Former users were not included. Social desirability bias may have influenced self-reported technology use and perceived value. In addition, the study was conducted within a specific rural public health context, and findings should be interpreted in light of local health care structures. Finally, detailed usage patterns and clinical outcomes associated with the digital health devices were not examined and warrant further investigation.

In this mixed methods study conducted in an underserved rural area of Korea, community-based mHealth services were successfully integrated into the daily lives of residents living with, or at risk for, chronic conditions. Mobile monitoring devices, supported by public health center nurses and peer networks, enabled participants to incorporate self-monitoring into their everyday routines.

Guided by the UTAUT2 framework, the findings identified performance expectancy, social influence, facilitating conditions, and habit as the most salient determinants of technology acceptance and continued use. Participants described perceiving tangible bodily changes, experiencing reduced isolation in managing chronic conditions, and relying on nurses as supportive “human-in-the-loop” partners operating in the background. Over time, these experiences helped participants establish stable and sustainable self-monitoring behaviors.

These results suggest that digital health strategies for rural and aging communities should move beyond simple device deployment. Instead, such strategies should invest in intuitive system design, hands-on onboarding, and ongoing nurse-involved technical and interpretive support. In addition, intentionally fostering group-based activities and peer connections may strengthen social influence and hedonic motivation. Finally, stable funding and sustained organizational commitment will be essential to maintaining trust, reducing digital inequality, and realizing the long-term benefits of remote monitoring–supported self-management in underserved rural populations.