Experience, Perception, and Attitudes Toward Decentralized Clinical Trial (DCT) Among Clinical Research Coordinators and Clinical Research Associates in South Korea: A Cross-Sectional Study

Experience, Perception, and Attitudes Toward Decentralized Clinical Trial (DCT) Among Clinical Research Coordinators and Clinical Research Associates in South Korea: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Experience, Perception, and Attitudes Toward Decentralized Clinical Trial (DCT) Among Clinical Research Coordinators and Clinical Research Associates in South Korea: A Cross-Sectional Study Seon Ji, Mangyeong Lee, Hae Sook Bok, Su Jin Kim, Juhee Cho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8416486/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Decentralized clinical trial (DCT)s have emerged as a promising approach to increase access, efficiency, and patient-centricity in clinical research. Although global regulatory bodies have encouraged the adoption of digital trial components such as electronic consent and remote monitoring, empirical evidence on workforce readiness and acceptance remains limited. This study aimed to evaluate experience, perceptions, and attitudes toward DCTs among South Korean clinical trial professionals. A cross-sectional survey was conducted with 383 clinical research professionals, including clinical research coordinator (CRC)s and clinical research associates (CRA)s. The questionnaire measured experience with eight core DCT component and assessed perceptions across seven domains, including feasibility, satisfaction, and outlook. Group differences were analyzed based on job role, organizational affiliation, career level, and prior DCT experience. Statistical analyses included Kruskal–Wallis, ANOVA, logistic regression, and linear regression. The most experienced DCT components were electronic informed consent (43.1%), remote enrollment (44.4%), and ePRO (43.1%). CRCs and mid-career professionals (13–60 months) reported higher engagement and more favorable perceptions compared to other groups. Regression analyses showed that prior DCT experience significantly predicted higher scores in perceived ease of implementation, satisfaction, and expansion expectations (β > 0.5). These findings underscore that DCT adoption is not solely a technical issue but depends on organizational context, job role, and experiential learning. Mid-career professionals represent a critical target group for capacity-building. Although the study focused on South Korea, the results may inform global efforts to design scalable and context-sensitive DCT implementation strategies. Clinical Trial Decentralized Clinical Trial DCT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Decentralized Clinical Trial(DCT)s have gained global attention as a transformative model that leverages digital technologies to enable remote consent, telehealth interventions, and virtual data capture.[ 1 – 3 ] Accelerated by the COVID-19 pandemic, this paradigm shift has been supported by temporary and permanent guidance from major regulatory agencies—including the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) and Ministry of Health, Labour and Welfare(MHLW) [ 4 – 7 ]—encouraging the adoption of tools such as electronic informed consent (eConsent), remote monitoring, and home drug delivery. As a result, the number of DCT-related publications and registered studies has increased markedly since 2020. Despite growing technical interest and institutional support, empirical evidence regarding how DCTs are operationalized in practice—and how they are perceived by frontline clinical trial professionals—remains limited. In particular, the concept of “decentralization” lacks consistent definition, and most prior literature fails to differentiate between the types, degrees, and real-world contexts of DCT implementation. Moreover, little is known about how core clinical trial personnel—especially Clinical Research Coordinator (CRC)s and Clinical Research Associate (CRA)s—experience this transition or perceive its implications for their work. South Korea presents a particularly instructive case in this global context. As a country with advanced digital infrastructure and rapidly evolving clinical trial regulations, it stands at a pivotal point between technological readiness and institutional adaptation. [ 8 ] Regulatory discussions on DCTs are actively underway, and government-led initiatives have signaled growing openness to remote and hybrid trial models. [ 9 ] Yet, empirical research assessing how trial professionals interpret, adopt, or resist decentralized practices remains scarce. Understanding these professional perspectives is critical for shaping both national and international policy frameworks. As DCT adoption continues to expand worldwide, insights from digitally mature yet regulatorily cautious settings like Korea offer valuable lessons for balancing innovation with real-world feasibility and workforce alignment. This study addresses this gap by analyzing the experience, perceptions, and attitudes toward DCTs among Korean CRCs and CRAs. Guided by the PRECEDE model [ 10 – 11 ], this research further examines how prior exposure to DCTs and workplace context shape acceptance, resistance, and perceived implementation barriers—findings that may inform strategic diffusion efforts not only in Korea but also across other health systems seeking to institutionalize DCTs. Methods This study is a cross-sectional study which surveyed CRCs and CRAs employed in clinical trials in South Korea to assess their experiences, perceptions, and attitudes toward DCT, and analyzed whether these differed by job role. [ 12 – 14 ] The interaction among experience, perception, and attitude was also explored. The questionnaire was developed based on DCT component extracted from existing literature on decentralized trials; remote recruitment and enrollment, electronic informed consent form (ICF), direct delivery of investigational product to patient, digital interventions, remote interventions, remote data collection, electronic Patient Reported Outcome (PRO) and remote clinical outcome assessments (COA). This likely reflects higher regulatory, logistical, and ethical complexity associated with these components. Notably, 27.7% of respondents reported experience with remote data collection, suggesting greater feasibility of remote observation compared to remote treatment. Items covering experience, perception, and attitude toward each DCT component were grounded in established theories including the Technology Acceptance Model (Davis, 1989), Diffusion of Innovation Theory (Rogers, 1962), and Innovation Resistance Theory (Ram, 1987) [ 15 – 18 ] (Supplementary material 1). According to the 2022 survey on Korean clinical trial workforce, approximately 15,000 individuals are involved across domestic sponsors, contract research organizations (CROs), and clinical trial sites.[ 19 ] Targeting a 95% confidence interval and 5% margin of error, a sample size of about 400 was planned. Participants were recruited via convenience sampling among clinical trial professionals accessible through scientific meetings and networks. To reflect adequate trial experience, personnel with less than six months of relevant work experience were excluded. Job roles included CRCs working at trial sites under investigator delegation, CRAs managing trial execution at CROs, and CRAs working for domestic and multinational sponsors. Data were collected via an online survey using Microsoft Forms. Screening questions ensured exclusion criteria were applied. Partially completed responses and those failing inclusion criteria were removed. Variables included experience with eight DCT components (binary yes/no), and perceptions and attitudes measured on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree). Descriptive statistics summarized participant characteristics and outcome variables. Normality was assessed to select appropriate statistical tests, including Kruskal–Wallis, ANOVA, logistic regression, and linear regression analyses. Statistical analyses were performed using R version 4.5.0, with significance set at p < .05 and adjusted p-values applied for multiple comparisons. Results Participant Characteristics A total of 389 responses were collected; after excluding 6 ineligible responses (duplicates, < 6 months experience), 383 valid responses were analyzed. (Table 1 ). Table 1 Participant Characteristics A total of 389 responses were collected and 383 valid responses were analyzed. Characteristic N(%) Factor 1. Gender Female 359(93.7%) Male 24(6.3%) Factor 2. Age <30 years 105(27.4%) 30–34 years 94(24.5%) ≥35 years 184(48.0%) Factor 3. Job Role Clinical Research Coordinator (CRC) 209(54.6%) Clinical Research Associate (CRA) 174(45.4%) Factor 4. Work Environment Clinical Trial Site 209(54.6%) CRO 115(30.0%) Domestic Pharma/Medtech Companies 42(11.0%) Multinational Pharma/Medtech Companies 17(4.4%) Factor 5. Career Length (Years of Experience on Clinical Trial) 6–12 months 18(4.7%) 13–36 months 153(39.9%) 37–60 months 127(33.2%) ≥61 months 85(22.2%) The majority of respondents were female (n = 359, 93.7%), with males accounting for only 24 (6.3%). Most participants belonged to a younger age group, with 51.96% under 35 years old and 27.42% under 30 years old. Birth years ranged from the late 1970s to the late 1990s, with a concentration in the late 1980s to early 1990s. Regarding job roles, CRC comprised 209 respondents (54.57%), and CRA accounted for 174 (45.43%). In terms of workplace environment, 209 (54.57%) worked at clinical trial sites, 115 (30.03%) at CROs, 42 (10.97%) at domestic pharmaceutical and medical device companies, and 17 (4.44%) at multinational pharmaceutical and medical device companies. By career duration, 153 respondents (39.95%) belonged to the 6–36 months experience group, and the remainder were relatively evenly distributed across other experience levels. These characteristics indicate that the sample broadly reflects the core operational roles of CRCs and CRAs in Korea’s clinical trial landscape. Experience with DCT Components Experience with DCT components was unevenly distributed. The most frequently reported components were eICF (43.1%), ePRO (43.1%), and remote recruitment or enrollment (44.4%). These components are relatively less complex technologically and operationally, making them easier to implement alongside conventional trials. In contrast, experience rates were lower for digital interventions (24.3%), remote interventions (19.8%), direct delivery of investigational product to patient (19.3%), and remote COA (21.9%). This likely reflects higher regulatory, logistical, and ethical complexity associated with these components. Notably, 27.7% of respondents reported experience with remote data collection, suggesting greater feasibility of remote observation compared to remote treatment. Experience with DCT Components by Job Role CRCs reported significantly higher experience rates across most DCT components compared to CRAs. (Table 2 ) For instance, experience with remote recruitment/enrollment was 66.0% among CRCs versus 18.4% among CRAs; eICF experience was 63.2% versus 19.0%; digital intervention 34.9% versus 11.5%; remote intervention 30.6% versus 6.9%; and direct delivery of investigational product to patient 30.1% versus 6.3%. ePRO was the only component with similar experience rates between the two groups (43.7% vs. 42.6%). Table 2 Experience with DCT Components by Job Role Experience with distributed clinical trial components was categorized by job role (CRA and CRC). DCT Component Remote recruitment and enrollment Electronic informed consent (eICF) Direct delivery of investigational product to patient: Direct to Patient Digital interventions Remote interventions Remote collection of trial outcomes Electronic patient-reported outcomes (ePRO) Remote clinical-outcome assessment (COA) CRA Experience (%) 18.4 19 6.3 11.5 6.9 18.4 43.7 10.3 CRC Experience (%) 66 63.2 30.1 34.9 30.6 35.4 42.6 31.6 T-Test p-value p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 0.911 p < 0.001 Cramér’s V 0.477 0.444 0.3 0.272 0.296 0.189 0.011 0.256 Chi-square tests revealed significant associations for most DCT components (p < .05), with Cramer’s V indicating moderate to strong effects for remote recruitment (V = 0.477) and eICF (V = 0.444), and weaker effects for remote clinical assessment (V = 0.256) and remote data collection (V = 0.189). No significant association was found for ePRO (V = 0.011, p = 0.911) Experience with DCT Components by Work Environment Among the four work environments; clinical trial sites, CROs, domestic pharmaceutical/medical device companies, and multinational pharmaceutical/medical device companies; (Table 3 ) Table 3 Experience with DCT Components by work environment Experience with distributed clinical trial components was categorized by work environment (clinical trial sites, CROs, domestic pharmaceutical/medical device companies (Domestic Pharma/MedTech), and multinational pharmaceutical/medical device companies (Multinational Pharma/MedTech) DCT Component Remote recruitment and enrollment Electronic informed consent (eICF) Direct delivery of investigational product to patient: Direct to Patient Digital interventions Remote interventions Remote collection of trial outcomes Electronic patient-reported outcomes (ePRO) Remote clinical-outcome assessment (COA) Clinical Trial Site (%) 66 63.2 30.1 34.9 30.6 35.4 42.6 31.6 CRO (%) 8.7 8.7 7 11.3 6.1 18.3 47.8 12.2 Domestic Pharma/MedTech (%) 45.2 42.9 7.1 11.9 11.9 11.9 19 2.4 Multinational Pharma/MedTech (%) 17.6 29.4 0 11.8 0 35.3 76.5 17.6 Chi-square p-value p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 Cramér’s V 0.521 0.488 0.303 0.272 0.302 0.211 0.221 0.267 The site staff (in South Korea, CRC) reported the highest experience rates in most DCT component. For example, 66.0% of site staff had experience with remote recruitment and enrollment, compared to 8.7% for CROs, 17.6% for multinational companies, and 45.2% for domestic companies. Similar patterns were observed for eICF, digital intervention, and remote intervention. However, ePRO experience was highest among multinational company staff (76.5%). Chi-square tests showed significant differences by work environment (p < .001), with Cramer’s V ranging from strong effects (remote recruitment and enrollment: V = 0.521) to weaker effects (ePRO: V = 0.221). Logistic regression indicated that site staff had significantly higher odds of reporting DCT experience, while multinational company employees showed higher odds of ePRO experience (OR = 4.38, 95% CI: 1.49–16.0) Experience with DCT Components by Career Level Participants with 6–36 months and 37–60 months of experience demonstrated the highest experience rates across most DCT components. Conversely, those with more than 61 months of experience showed relatively lower participation, especially in digital intervention (12.9%), remote intervention (8.2%), and remote COA (16.5%). The 6–12 months group generally had the lowest experience rates. (Table 4 ) Table 4 Experience with DCT Components by Career Level Experience with distributed clinical trial components was categorized by Career Level (6–12 Months, 13–36 Months, 37–60 Months, exceeding 61 Months) DCT Component Remote recruitment and enrollment Electronic informed consent (eICF) Direct delivery of investigational product to patient: Direct to Patient Digital interventions Remote interventions Remote collection of trial outcomes Electronic patient-reported outcomes (ePRO) Remote clinical-outcome assessment (COA) 6–12 months (%) 38.9 33.3 5.6 16.7 5.6 11.1 11.1 5.6 13–36 months(%) 47.1 42.5 17 22.2 17 22.2 34.6 17.6 37–60 months(%) 52 52 31.5 35.4 33.1 37.8 48 33.1 61 + months(%) 29.4 32.9 8.2 12.9 8.2 25.9 57.6 16.5 p-value 0.01 0.0389 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 p < 0.001 Cramér’s V 0.172 0.148 0.237 0.201 0.252 0.172 0.232 0.199 Chi-square tests confirmed significant differences among experience groups (p < .05), with Cramer’s V indicating small to moderate effects (e.g., remote intervention V = 0.252). Logistic regression results showed that the 37–60 months experience group had significantly higher odds of having DCT experience (e.g., remote COA OR = 8.40, p < .01) . Perceptions and Attitudes Toward DCT Components Participants’ perceptions and attitudes toward DCT components were assessed across 16 Likert-scale items grouped into seven domains: (1) feasibility and ease of use (items 1–2), (2) data reliability and privacy (items 3–4), (3) perceived social benefits (item 5), (4) complexity and communication burden (items 6–8), (5) satisfaction and compensation (items 9–11), (6) knowledge and system readiness (items 12–14), and (7) future outlook (items 15–16). Mean scores for eight key DCT components were calculated. Among feasibility-related items, ePRO received the highest scores (item 1 mean = 3.25, item 2 mean = 3.14), whereas remote clinical outcome assessment (COA) scored relatively lower on feasibility (item 2 mean = 3.08), indicating that ePRO is perceived as the most user-friendly and operationally efficient DCT modality. All DCT components scored above 3.0 in data reliability and privacy domains, with ePRO highest in privacy (item 4 mean = 3.10). Remote and physical interventions scored slightly lower (remote 3.02, physical 2.98). Perceived social benefits were consistently rated highly across all components (mean 3.27–3.32), with remote COA scoring the highest (3.32), reflecting recognition of DCT’s potential to enhance participant diversity and accessibility. Complexity and communication burden items scored above 3.0 for communication with researchers (item 6) and participants (item 7), but implementation difficulty (item 8) was rated lower for all components, especially remote intervention (2.82), physical intervention (2.87), and remote data collection (2.95), indicating perceived operational challenges. Satisfaction (item 9) was highest for remote (3.32) and physical interventions (3.31), while economic compensation (item 11) received consistently low scores across all components (mean 2.2–2.3), suggesting widespread dissatisfaction with financial incentives related to DCT implementation. Knowledge and system infrastructure items (items 12–14) scored between 2.5 and 2.8. Remote COA scored lowest in information accessibility (2.66), highlighting the need for infrastructure improvement and guideline development. Future outlook items (15–16) reflected generally positive views on DCT expansion, with ePRO receiving the highest scores for expected future use (item 15 mean = 3.33) and anticipated diffusion (item 16 mean = 3.26), indicating strong confidence in its scalability and institutional acceptance. Perception scores for each DCT component across the 16 items are visually summarized in Fig. 1 – 3 , with detailed means provided in Supplementary Material 2. Perception Differences by Job Role To examine whether DCT perceptions differed by job role (CRC vs. CRA), independent samples t-tests and Mann-Whitney U tests were conducted for the 16 Likert items, accounting for ordinal scale characteristics and potential violations of normality assumptions. Overall, CRCs reported significantly more favorable perceptions of DCT than CRAs Significant role-based differences were observed across most DCT component. (Fig. 3 ) For remote recruitment, 14 of 16 perception items differed significantly (p < .05), with CRCs rating feasibility (p < .001), data protection (p < .001), and satisfaction (p < .001) higher than CRAs. Similarly, for eICF, 14 items showed significant differences, particularly in feasibility, infrastructure readiness, and ease of use (all p < .001). In remote physical intervention, 13 items significantly differed (p < .05), notably in feasibility (p < .01), satisfaction (p < .001), and infrastructure (p < .001), with “feasibility without site visits” being the only nonsignificant item. For digital interventions, 12 items differed significantly, with CRCs giving higher ratings on feasibility (p < .01), ease of use (p < .01), satisfaction (p < .001), and information accessibility (p < .001). In remote interventions, 11 items showed significant differences (p < .05), mainly in participant communication burden (p < .001), feasibility (p < .01), satisfaction (p < .001), and infrastructure (p < .001). Regarding remote data collection, significant differences were identified in 10 of 16 items, with CRCs consistently reporting higher work efficiency, ease of performance, satisfaction, and information accessibility (p < .001). For ePRO, 13 items differed significantly between groups across both t-test and Mann–Whitney analyses, with CRCs showing more favorable perceptions on feasibility, implementation, satisfaction, and readiness (p < .01 to p < .001). Lastly, remote clinical outcome assessments (COA) showed significant differences in 12 items, with CRCs more optimistic about feasibility, implementation, and future use (p < .01), whereas perceptions of social value and privacy concerns were comparable between groups. These results consistently indicate that CRCs hold more positive perceptions regarding key DCT components, particularly in operational feasibility, ease of implementation, and infrastructure, reflecting their more active involvement and direct experience with DCT procedures. Detailed means and statistical comparisons by job role are provided in Supplementary Material 3. Perception Differences by Work Environment ANOVA was conducted using 16 Likert items to determine perception differences by work environment (clinical trial site, CRO, domestic pharmaceutical/medical device company, multinational pharmaceutical/medical device company). Significant differences were observed across most items and DCT components (Fig. 4 ) For remote recruitment, 15 of 16 items differed significantly by work environment (p < .05), including feasibility, ease of implementation, data reliability, privacy, operational complexity, communication burden, satisfaction, compensation, knowledge, infrastructure, and future expectations. Social value and investigator communication demands did not differ significantly (p > .05). Kruskal-Wallis tests corroborated these findings. Tukey HSD post-hoc analysis revealed that clinical site staff scored significantly higher on most dimensions compared to other groups. For example, site staff rated feasibility significantly higher than CRO staff (items 1 and 2, p < .001). Domestic company staff scored higher than multinational company staff on feasibility (item 1, p = .027). In data reliability and privacy domains, site experts scored significantly higher than others, and domestic companies scored higher than multinational companies (item 3, p = .0083). Feasibility of implementation (item 8) was superior among site staff versus all other groups (p < .001), with domestic companies outperforming multinationals (p = .0004). Similar trends were seen for satisfaction, career utility, and financial incentives. In knowledge and infrastructure domains (items 12–16), site staff showed the most optimism, with domestic companies scoring significantly higher than multinationals (p < .001, p = .014). Similar patterns emerged for eICF, Direct Delivery to Patient, digital intervention, remote intervention, remote outcome collection, ePRO, and remote clinical outcome assessment, with clinical site staff and domestic company employees consistently reporting more positive perceptions than CRO or multinational company employees. Detailed means and statistical comparisons by job role are provided in Supplementary Material 4. Perception Differences by Career Level Respondents were categorized into four career experience groups (6–12 Months, 13–36 months, 37–60 months, 61 months or more), and ANOVA and Kruskal-Wallis tests assessed perception differences across the 16 items for each DCT component. (Fig. 5 ) For remote recruitment and enrollment, 14 of 16 items differed significantly by career group (p < .05). The 37–60 month group consistently showed the highest perception scores across most items, including feasibility, ease of implementation, satisfaction, compensation, and future expectations, while the 61 + month group scored the lowest. Similar trends were observed for eICF, with significant differences in 15 items; the 37–60 month group scored higher in time savings, data reliability, and regulatory familiarity, while the 61 + month group scored lower. For remote physical intervention, 13 items showed significant differences, particularly feasibility, satisfaction, compensation, and future outlook. Detailed means and statistical comparisons by job role are provided in Supplementary Material 5. Prioritization of DCT Component Adoption A total of 383 respondents were asked to select up to three DCT components they believed should be prioritized for implementation. The most frequently selected item was eICF, receiving 502 selections (43.7%). This indicates a strong consensus regarding the importance of remote consent systems as a foundational tool for participant engagement in decentralized trial settings. The second most selected component was remote enrollment, with 350 responses (30.5%), reflecting widespread interest in improving accessibility and participant convenience. (Supplementary material 6) Differences emerged across job roles. While eICF was the top-ranked priority for both CRAs and CRCs, CRAs selected it at a higher rate (27.2% vs. 23.7%). CRAs also prioritized ePRO (17.3%) more than CRCs (7.7%), suggesting a greater emphasis on remote data collection and monitoring. Conversely, CRCs selected remote enrollment at nearly twice the rate of CRAs (22.1% vs. 11.5%), highlighting their focus on direct participant interaction. CRCs also gave higher priority to digital and remote interventions, likely reflecting their operational roles in treatment delivery. Across all work environments, eICF was consistently ranked as the top priority: clinical trial sites (24.6%), CROs (25.7%), multinational companies (28.3%), and domestic pharmaceutical companies (30.9%). Site-based staff also showed high preference for remote enrollment (23.0%) and digital interventions (16.4%). CRO staff favored ePRO (20.4%), likely due to their emphasis on remote monitoring and efficiency. Multinational staff prioritized eICF and remote interventions (28.3%, 17.4%, respectively), while domestic company staff showed stronger preference for digital interventions (22.7%) and eICF (30.9%). All career groups identified eICF as the top priority, with selection rates increasing with experience (from 13.5% in the 6–12 months group to 26.1% in the 61 + month group). This trend suggests that senior professionals place greater emphasis on regulatory compliance and procedural integrity. The 6–12 months group favored remote (21.2%) and digital interventions (19.2%), indicating openness to new technologies. Mid-career professionals (13–60 months) showed a balanced distribution of preferences, with the 37–60 months group prioritizing digital interventions (19.8%). The most experienced group (61 + months) prioritized eICF (26.1%), ePRO (17.7%), and remote interventions (14.8%), reflecting a focus on procedural stability and patient-centered tools. Perceived Prerequisites for Successful DCT Adoption To identify perceived prerequisites for DCT implementation, respondents were asked to select multiple key enabling factors. The most cited prerequisite was regulatory guidance and approval, chosen by 579 respondents (75.7%), followed by digital tool development (511 responses, 66.9%) and stakeholder training (421 responses, 55.1%). These findings highlight the need for regulatory clarity, technological infrastructure, and human resource preparedness. (Supplementary material 7) Other notable factors included appropriate compensation for participants (179 responses) and compensation for implementers (176 responses), underscoring the perceived need for fair incentive structures. Ethical concerns were rarely selected (1.6%), suggesting low prioritization relative to structural or operational challenges. Both CRAs and CRCs most frequently selected regulatory guidance, tool development, and training. CRAs emphasized training more strongly (23.9% vs. 19.4%), while research nurses prioritized implementer compensation (11.4% vs. 6.4%), reflecting their frontline operational roles. All work environments emphasized the same top three prerequisites. Clinical site staff prioritized regulatory guidance (29.4%) and tool development (25.8%), while CRO staff emphasized training (25.3%). Multinational and domestic company staff also prioritized regulatory frameworks (> 33%), with domestic company staff placing the highest value on digital tool development (28.4%). . All experience levels emphasized regulatory guidance, tool development, and training. Junior professionals (6–12 months) prioritized ethics and compensation, reflecting a focus on support and protection. Mid-career professionals showed a balanced view, while senior staff (61 + months) focused heavily on regulatory and technical infrastructure. These results suggest that while the broader consensus emphasizes regulatory and technical readiness, tailored strategies may be needed across job roles, environments, and career levels to support successful DCT adoption. Discussion This study examined the acceptance of DCTs among clinical trial professionals in South Korea, using a cross-sectional survey informed by implementation science frameworks. We explored how experience, perception, and attitude toward DCTs vary by job role, organizational environment, and career level, and how these factors interact to shape readiness for DCT adoption. Our findings highlight that CRCs report significantly greater exposure to DCT components than CRAs, particularly for component involving direct participant interaction. This may reflect the Korean clinical environment, where nurses often manage a broader range of concurrent trials and are more frequently involved in consent and intervention delivery. Mid-career professionals (13–60 months), particularly those in the 37–60 months range, demonstrated the highest likelihood of engaging with advanced DCT components. While this finding may be influenced by cumulative exposure, it is notable that their engagement exceeded that of professionals with over 61 months of experience, suggesting that operational roles and openness to innovation may be more influential than tenure alone. However, interpretation should be cautious due to uneven group sizes. Perception scores reflected optimism toward the feasibility and benefits of DCTs, especially for digital tools like eConsent and ePRO. Nonetheless, concerns about regulatory clarity, data protection, and compensation remain. CRCs and mid-career professionals expressed significantly more favorable perceptions than their counterparts, and regression models showed that prior experience was a strong predictor of positive attitudes—especially for implementation ease, satisfaction, and information access. In line with the PRECEDE–PROCEED model, it was identified key behavioral and environmental determinants of DCT adoption. Predisposing factors included differences in knowledge and beliefs by role and experience; enabling factors included regulatory guidance, digital tool development, and stakeholder education; and reinforcing factors were strongly linked to hands-on experience. It also was found evidence of resistance mechanisms, including psychological resistance among senior staff and functional resistance related to implementation complexity and misalignment with existing roles. These results support a phased implementation approach that begins with low-barrier DCT components, offers experiential learning opportunities, and incorporates tailored support by job function and career level. Mid-career professionals—particularly those with 13–36 months of experience—emerged as key targets for institutional diffusion strategies. For high-complexity components, simulation training, case archives, and pilot trials may mitigate resistance and build operational confidence. Importantly, while the study focused on South Korea, its insights have global relevance. The structural and behavioral dynamics observed are likely to apply in other hybrid or transition-phase health systems, positioning this research as a valuable reference for international DCT policy development. Although the survey assessed cumulative experience, and comparisons between lower and mid-level groups should be interpreted cautiously, it is notable that the mid-career group outperformed even the most experienced professionals. Sampling imbalances between career groups should also be considered. Conclusion This study provides a multidimensional assessment of the Korean clinical trial workforce’s readiness for DCT implementation, elucidating the influence of job role, work environment, experience, and exposure on preparedness and resistance. The adoption of DCT is influenced not only by technical availability but also by institutional, behavioral, and experiential alignment. Findings highlight the necessity of role- and phase-specific training, infrastructure investment, and experience-based implementation strategies. Policymakers and organizational leaders should prioritize regulatory clarity, tool development, and capacity building to ensure sustainable and equitable DCT integration. Limitations include potential self-selection bias, unequal subgroup sizes, and sampling from specific study sites; however, the study offers a robust empirical foundation to enhance DCT readiness globally, including in Korea. Future research should expand to include sponsor stakeholders, trial designers, and longitudinal tracking of evolving perceptions and implementation outcomes. Overall, this evidence-based, practice-oriented policy roadmap underscores the importance of addressing real-world operational contexts to strategically expand decentralized clinical trials. Declarations Acknowledgments Funding Declaration This research was supported by the SmartTech Clinical Research Center (SCRC), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : RS-2023-KH142023). Clinical Trial Number: Not Applicable Ethical Approval The study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2025-01-069 ). Online informed consent was obtained from all participants prior to data collection, and all responses were anonymized. The study adhered to ethical standards, ensuring voluntary participation, confidentiality, and data protection. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Conceptualization: Seon Ji, Juhee Cho Methodology: Seon Ji, Juhee Cho, Mangyeong Lee Formal analysis: Seon Ji Writing – original draft: Seon Ji Writing – review & editing: Seon Ji, Juhee Cho, Man Gyeong Lee, Hae Sook Bok, Su Jin Kim Supervision: Juhee Cho All authors read and approved the final manuscript. References Underhill C, Freeman J, Dixon J, et al. 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Transl Clin Pharmacol. 2019;27(4):115–118. https://doi.org/10.12793/tcp.2019.27.4.115 Chung WK, Huh KY, Park J, Oh J, Yu KS. Establishment of Advanced Regulatory Innovation for Clinical Trials Transformation (ARICTT): a multi-stakeholder public-private partnership-based organization to accelerate the transformation of clinical trials. Transl Clin Pharmacol. 2024;32(1):30. Green LW, Kreuter MW. Health Program Planning: An Educational and Ecological Approach. 4th ed. New York: McGraw-Hill; 2005. Porter CM. Revisiting Precede–Proceed: A leading model for ecological and ethical health promotion. Health Educ J. 2015;75(6):753–764. Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest. 2020;158(1S):S65-S71. https://doi.org/10.1016/j.chest.2020.03.012 Health Knowledge. Design, applications, strengths and weaknesses of cross-sectional studies [Internet]. [cited 2023]. Available from: https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/cs-as-is Thomas L. Cross-sectional study | Definition, uses & examples. Scribbr. June 22, 2023 [cited 2023]. Available from: https://www.scribbr.com/methodology/cross-sectional-study/ Rogers EM. Diffusion of innovations. New York: Free Press of Glencoe; 1962. Rogers EM. Diffusion of Innovations. 5th ed. New York: Free Press; 2003. Ram S. A model of innovation resistance. Adv Consum Res. 1987;14:208–212. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–340. https://doi.org/10.2307/249008 Korea National Enterprise for Clinical Trials (KoNECT). 2022 Statistics Yearbook of the Korean Clinical Trial Industry. Seongnam: KoNECT; 2023. Available from: https://www.konect.or.kr/kr/board/konect_library_01/boardList.do Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 22 Dec, 2025 Submission checks completed at journal 22 Dec, 2025 First submitted to journal 21 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8416486","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581395079,"identity":"b02c7aef-efce-4cc4-9d27-6ea4fe1458a6","order_by":0,"name":"Seon Ji","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Seon","middleName":"","lastName":"Ji","suffix":""},{"id":581395080,"identity":"8bcc7baa-a12f-43b4-815c-5d152063f578","order_by":1,"name":"Mangyeong Lee","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Mangyeong","middleName":"","lastName":"Lee","suffix":""},{"id":581395081,"identity":"3b15544f-d0e9-44fb-99c8-e2800713593d","order_by":2,"name":"Hae Sook Bok","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Hae","middleName":"Sook","lastName":"Bok","suffix":""},{"id":581395082,"identity":"8a6b660d-a7ba-4462-91b2-c3eae91f6cb3","order_by":3,"name":"Su Jin Kim","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"Jin","lastName":"Kim","suffix":""},{"id":581395083,"identity":"1061691d-47b0-4cb7-a775-0a190dc092a1","order_by":4,"name":"Juhee Cho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYBACxgYeBoYPDAkGII4E0VoYZ5CkhYGBh4GZhyQtzO1nDz62+ZNmbHCA+eBtHqIc1pOXbJzblmNmcIAt2Zo4LTN4zKRzGypsDA4AGcRrsfgD0sL/jQQtDGwgh/GwEamlJ8fYsLctzVjyMJux5RxitBi2nzF88ONPsmHf8eaHN94QpaUBxmImRjkIyBOrcBSMglEwCkYwAAAsDCv1vB+tDwAAAABJRU5ErkJggg==","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":true,"prefix":"","firstName":"Juhee","middleName":"","lastName":"Cho","suffix":""}],"badges":[],"createdAt":"2025-12-21 10:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8416486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8416486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101459938,"identity":"fb6793fd-2c4e-4fa4-b331-905b286374e4","added_by":"auto","created_at":"2026-01-30 01:35:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125004,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Perception Score\u003c/p\u003e\n\u003cp\u003eParticipants’ perceptions and attitudes toward DCT components were assessed across 16 Likert-scale items grouped into seven domains: (1) feasibility and ease of use (items 1–2), (2) data reliability and privacy (items 3–4), (3) perceived social benefits (item 5), (4) complexity and communication burden (items 6–8), (5) satisfaction and compensation (items 9–11), (6) knowledge and system readiness (items 12–14), and (7) future outlook (items 15–16).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/93720cdb5afa6727ff16936d.png"},{"id":101459940,"identity":"020eed68-c277-4de2-9fb4-192a805719ef","added_by":"auto","created_at":"2026-01-30 01:35:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":473045,"visible":true,"origin":"","legend":"\u003cp\u003eThe Perception Scores for Each DCT Component across the 16 Items -Overall\u003c/p\u003e\n\u003cp\u003eMean scores for eight key DCT components were calculated. RC:Remote recruitment and enrollment, eICF:Electronic informed consent (eICF), DPT: Direct to Patient;Remote delivery of physical interventions, DI:Digital interventions, RI:Remote interventions, RC:Remote collection of trial outcomes, ePRO:Electronic patient-reported outcomes (ePRO), rCOA,Remote clinical-outcome assessment (COA)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/a66ed699f1bcb6e84c4cca1a.png"},{"id":101459939,"identity":"0515c924-862d-4c2b-a98d-371d076356e6","added_by":"auto","created_at":"2026-01-30 01:35:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1322576,"visible":true,"origin":"","legend":"\u003cp\u003ePerception Differences by Job Role.\u003c/p\u003e\n\u003cp\u003eThe question for participant communication burden is not associated with CRA, the question was excluded in the analysis for CRA response.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/5df1a57d8296dac6483a3443.png"},{"id":101751466,"identity":"0571e53b-9866-4d6f-9ae1-db3d9c837036","added_by":"auto","created_at":"2026-02-03 10:20:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2185467,"visible":true,"origin":"","legend":"\u003cp\u003ePerception Differences by work Environment\u003c/p\u003e\n\u003cp\u003eRespondents were categorized into four career experience groups (6–12 Months, 13-36 months, 37–60 months, 61 months or more).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/14f484504b29d69b2900c08a.png"},{"id":101459941,"identity":"f45429ce-8460-4f07-8d34-83aecc1b90f7","added_by":"auto","created_at":"2026-01-30 01:35:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1834323,"visible":true,"origin":"","legend":"\u003cp\u003ePerception Differences by work Environment\u003c/p\u003e\n\u003cp\u003eThe question for participant communication burden is not associated with CRA and, the question was excluded in the analysis for CRO, Domestic/Multinational pharmaceutical/medical device company.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/2948d5839ea682cd41359087.png"},{"id":101880528,"identity":"3cf8874e-4d32-4e5e-8060-b01836f97920","added_by":"auto","created_at":"2026-02-04 15:03:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6996348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8416486/v1/28ef510d-8aaf-44b4-8177-e68cff21d1c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experience, Perception, and Attitudes Toward Decentralized Clinical Trial (DCT) Among Clinical Research Coordinators and Clinical Research Associates in South Korea: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDecentralized Clinical Trial(DCT)s have gained global attention as a transformative model that leverages digital technologies to enable remote consent, telehealth interventions, and virtual data capture.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Accelerated by the COVID-19 pandemic, this paradigm shift has been supported by temporary and permanent guidance from major regulatory agencies\u0026mdash;including the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), Japan\u0026rsquo;s Pharmaceuticals and Medical Devices Agency (PMDA) and Ministry of Health, Labour and Welfare(MHLW) [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u0026mdash;encouraging the adoption of tools such as electronic informed consent (eConsent), remote monitoring, and home drug delivery. As a result, the number of DCT-related publications and registered studies has increased markedly since 2020.\u003c/p\u003e \u003cp\u003eDespite growing technical interest and institutional support, empirical evidence regarding how DCTs are operationalized in practice\u0026mdash;and how they are perceived by frontline clinical trial professionals\u0026mdash;remains limited. In particular, the concept of \u0026ldquo;decentralization\u0026rdquo; lacks consistent definition, and most prior literature fails to differentiate between the types, degrees, and real-world contexts of DCT implementation. Moreover, little is known about how core clinical trial personnel\u0026mdash;especially Clinical Research Coordinator (CRC)s and Clinical Research Associate (CRA)s\u0026mdash;experience this transition or perceive its implications for their work.\u003c/p\u003e \u003cp\u003eSouth Korea presents a particularly instructive case in this global context. As a country with advanced digital infrastructure and rapidly evolving clinical trial regulations, it stands at a pivotal point between technological readiness and institutional adaptation. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Regulatory discussions on DCTs are actively underway, and government-led initiatives have signaled growing openness to remote and hybrid trial models. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Yet, empirical research assessing how trial professionals interpret, adopt, or resist decentralized practices remains scarce.\u003c/p\u003e \u003cp\u003eUnderstanding these professional perspectives is critical for shaping both national and international policy frameworks. As DCT adoption continues to expand worldwide, insights from digitally mature yet regulatorily cautious settings like Korea offer valuable lessons for balancing innovation with real-world feasibility and workforce alignment. This study addresses this gap by analyzing the experience, perceptions, and attitudes toward DCTs among Korean CRCs and CRAs. Guided by the PRECEDE model [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], this research further examines how prior exposure to DCTs and workplace context shape acceptance, resistance, and perceived implementation barriers\u0026mdash;findings that may inform strategic diffusion efforts not only in Korea but also across other health systems seeking to institutionalize DCTs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is a cross-sectional study which surveyed CRCs and CRAs employed in clinical trials in South Korea to assess their experiences, perceptions, and attitudes toward DCT, and analyzed whether these differed by job role. [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] The interaction among experience, perception, and attitude was also explored.\u003c/p\u003e \u003cp\u003eThe questionnaire was developed based on DCT component extracted from existing literature on decentralized trials; remote recruitment and enrollment, electronic informed consent form (ICF), direct delivery of investigational product to patient, digital interventions, remote interventions, remote data collection, electronic Patient Reported Outcome (PRO) and remote clinical outcome assessments (COA). This likely reflects higher regulatory, logistical, and ethical complexity associated with these components. Notably, 27.7% of respondents reported experience with remote data collection, suggesting greater feasibility of remote observation compared to remote treatment.\u003c/p\u003e \u003cp\u003eItems covering experience, perception, and attitude toward each DCT component were grounded in established theories including the Technology Acceptance Model (Davis, 1989), Diffusion of Innovation Theory (Rogers, 1962), and Innovation Resistance Theory (Ram, 1987) [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (Supplementary material 1).\u003c/p\u003e \u003cp\u003eAccording to the 2022 survey on Korean clinical trial workforce, approximately 15,000 individuals are involved across domestic sponsors, contract research organizations (CROs), and clinical trial sites.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Targeting a 95% confidence interval and 5% margin of error, a sample size of about 400 was planned.\u003c/p\u003e \u003cp\u003eParticipants were recruited via convenience sampling among clinical trial professionals accessible through scientific meetings and networks. To reflect adequate trial experience, personnel with less than six months of relevant work experience were excluded. Job roles included CRCs working at trial sites under investigator delegation, CRAs managing trial execution at CROs, and CRAs working for domestic and multinational sponsors.\u003c/p\u003e \u003cp\u003eData were collected via an online survey using Microsoft Forms. Screening questions ensured exclusion criteria were applied. Partially completed responses and those failing inclusion criteria were removed.\u003c/p\u003e \u003cp\u003eVariables included experience with eight DCT components (binary yes/no), and perceptions and attitudes measured on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 4\u0026thinsp;=\u0026thinsp;strongly agree). Descriptive statistics summarized participant characteristics and outcome variables. Normality was assessed to select appropriate statistical tests, including Kruskal\u0026ndash;Wallis, ANOVA, logistic regression, and linear regression analyses. Statistical analyses were performed using R version 4.5.0, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;.05 and adjusted p-values applied for multiple comparisons.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 389 responses were collected; after excluding 6 ineligible responses (duplicates, \u0026lt;\u0026thinsp;6 months experience), 383 valid responses were analyzed. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eParticipant Characteristics\u003c/b\u003e A total of 389 responses were collected and 383 valid responses were analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor 1. Gender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359(93.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor 2. Age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(27.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(24.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184(48.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor 3. Job Role\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Research Coordinator (CRC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209(54.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Research Associate (CRA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174(45.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor 4. Work Environment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Trial Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209(54.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115(30.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic Pharma/Medtech Companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultinational Pharma/Medtech Companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(4.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFactor 5. Career Length (Years of Experience on Clinical Trial)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(4.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;36 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(39.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u0026ndash;60 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(33.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;61 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe majority of respondents were female (n\u0026thinsp;=\u0026thinsp;359, 93.7%), with males accounting for only 24 (6.3%). Most participants belonged to a younger age group, with 51.96% under 35 years old and 27.42% under 30 years old. Birth years ranged from the late 1970s to the late 1990s, with a concentration in the late 1980s to early 1990s.\u003c/p\u003e \u003cp\u003eRegarding job roles, CRC comprised 209 respondents (54.57%), and CRA accounted for 174 (45.43%). In terms of workplace environment, 209 (54.57%) worked at clinical trial sites, 115 (30.03%) at CROs, 42 (10.97%) at domestic pharmaceutical and medical device companies, and 17 (4.44%) at multinational pharmaceutical and medical device companies.\u003c/p\u003e \u003cp\u003eBy career duration, 153 respondents (39.95%) belonged to the 6\u0026ndash;36 months experience group, and the remainder were relatively evenly distributed across other experience levels. These characteristics indicate that the sample broadly reflects the core operational roles of CRCs and CRAs in Korea\u0026rsquo;s clinical trial landscape.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperience with DCT Components\u003c/h3\u003e\n\u003cp\u003eExperience with DCT components was unevenly distributed. The most frequently reported components were eICF (43.1%), ePRO (43.1%), and remote recruitment or enrollment (44.4%). These components are relatively less complex technologically and operationally, making them easier to implement alongside conventional trials.\u003c/p\u003e \u003cp\u003eIn contrast, experience rates were lower for digital interventions (24.3%), remote interventions (19.8%), direct delivery of investigational product to patient (19.3%), and remote COA (21.9%). This likely reflects higher regulatory, logistical, and ethical complexity associated with these components. Notably, 27.7% of respondents reported experience with remote data collection, suggesting greater feasibility of remote observation compared to remote treatment.\u003c/p\u003e\n\u003ch3\u003eExperience with DCT Components by Job Role\u003c/h3\u003e\n\u003cp\u003eCRCs reported significantly higher experience rates across most DCT components compared to CRAs. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) For instance, experience with remote recruitment/enrollment was 66.0% among CRCs versus 18.4% among CRAs; eICF experience was 63.2% versus 19.0%; digital intervention 34.9% versus 11.5%; remote intervention 30.6% versus 6.9%; and direct delivery of investigational product to patient 30.1% versus 6.3%. ePRO was the only component with similar experience rates between the two groups (43.7% vs. 42.6%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eExperience with DCT Components by Job Role\u003c/b\u003e Experience with distributed clinical trial components was categorized by job role (CRA and CRC).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCT Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote recruitment and enrollment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectronic informed consent (eICF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect delivery of investigational product to patient: Direct to Patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRemote interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRemote collection of trial outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eElectronic patient-reported outcomes (ePRO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRemote clinical-outcome assessment (COA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRA Experience (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRC Experience (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-Test p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCram\u0026eacute;r\u0026rsquo;s V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChi-square tests revealed significant associations for most DCT components (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), with Cramer\u0026rsquo;s V indicating moderate to strong effects for remote recruitment (V\u0026thinsp;=\u0026thinsp;0.477) and eICF (V\u0026thinsp;=\u0026thinsp;0.444), and weaker effects for remote clinical assessment (V\u0026thinsp;=\u0026thinsp;0.256) and remote data collection (V\u0026thinsp;=\u0026thinsp;0.189). No significant association was found for ePRO (V\u0026thinsp;=\u0026thinsp;0.011, p\u0026thinsp;=\u0026thinsp;0.911)\u003c/p\u003e\n\u003ch3\u003eExperience with DCT Components by Work Environment\u003c/h3\u003e\n\u003cp\u003eAmong the four work environments; clinical trial sites, CROs, domestic pharmaceutical/medical device companies, and multinational pharmaceutical/medical device companies; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eExperience with DCT Components by work environment\u003c/b\u003e Experience with distributed clinical trial components was categorized by work environment (clinical trial sites, CROs, domestic pharmaceutical/medical device companies (Domestic Pharma/MedTech), and multinational pharmaceutical/medical device companies (Multinational Pharma/MedTech)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCT Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote recruitment and enrollment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectronic informed consent (eICF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect delivery of investigational product to patient: Direct to Patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRemote interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRemote collection of trial outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eElectronic patient-reported outcomes (ePRO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRemote clinical-outcome assessment (COA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Trial Site (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRO (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic Pharma/MedTech (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultinational Pharma/MedTech (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCram\u0026eacute;r\u0026rsquo;s V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe site staff (in South Korea, CRC) reported the highest experience rates in most DCT component. For example, 66.0% of site staff had experience with remote recruitment and enrollment, compared to 8.7% for CROs, 17.6% for multinational companies, and 45.2% for domestic companies. Similar patterns were observed for eICF, digital intervention, and remote intervention. However, ePRO experience was highest among multinational company staff (76.5%).\u003c/p\u003e \u003cp\u003eChi-square tests showed significant differences by work environment (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with Cramer\u0026rsquo;s V ranging from strong effects (remote recruitment and enrollment: V\u0026thinsp;=\u0026thinsp;0.521) to weaker effects (ePRO: V\u0026thinsp;=\u0026thinsp;0.221). Logistic regression indicated that site staff had significantly higher odds of reporting DCT experience, while multinational company employees showed higher odds of ePRO experience (OR\u0026thinsp;=\u0026thinsp;4.38, 95% CI: 1.49\u0026ndash;16.0)\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eExperience with DCT Components by Career Level\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eParticipants with 6\u0026ndash;36 months and 37\u0026ndash;60 months of experience demonstrated the highest experience rates across most DCT components. Conversely, those with more than 61 months of experience showed relatively lower participation, especially in digital intervention (12.9%), remote intervention (8.2%), and remote COA (16.5%). The 6\u0026ndash;12 months group generally had the lowest experience rates. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eExperience with DCT Components by Career Level\u003c/b\u003e Experience with distributed clinical trial components was categorized by Career Level (6\u0026ndash;12 Months, 13\u0026ndash;36 Months, 37\u0026ndash;60 Months, exceeding 61 Months)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCT Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote recruitment and enrollment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectronic informed consent (eICF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect delivery of investigational product to patient: Direct to Patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRemote interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRemote collection of trial outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eElectronic patient-reported outcomes (ePRO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRemote clinical-outcome assessment (COA)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;12 months (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;36 months(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u0026ndash;60 months(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026thinsp;+\u0026thinsp;months(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCram\u0026eacute;r\u0026rsquo;s V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChi-square tests confirmed significant differences among experience groups (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), with Cramer\u0026rsquo;s V indicating small to moderate effects (e.g., remote intervention V\u0026thinsp;=\u0026thinsp;0.252). Logistic regression results showed that the 37\u0026ndash;60 months experience group had significantly higher odds of having DCT experience (e.g., remote COA OR\u0026thinsp;=\u0026thinsp;8.40, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) .\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePerceptions and Attitudes Toward DCT Components\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; perceptions and attitudes toward DCT components were assessed across 16 Likert-scale items grouped into seven domains: (1) feasibility and ease of use (items 1\u0026ndash;2), (2) data reliability and privacy (items 3\u0026ndash;4), (3) perceived social benefits (item 5), (4) complexity and communication burden (items 6\u0026ndash;8), (5) satisfaction and compensation (items 9\u0026ndash;11), (6) knowledge and system readiness (items 12\u0026ndash;14), and (7) future outlook (items 15\u0026ndash;16). Mean scores for eight key DCT components were calculated.\u003c/p\u003e \u003cp\u003eAmong feasibility-related items, ePRO received the highest scores (item 1 mean\u0026thinsp;=\u0026thinsp;3.25, item 2 mean\u0026thinsp;=\u0026thinsp;3.14), whereas remote clinical outcome assessment (COA) scored relatively lower on feasibility (item 2 mean\u0026thinsp;=\u0026thinsp;3.08), indicating that ePRO is perceived as the most user-friendly and operationally efficient DCT modality.\u003c/p\u003e \u003cp\u003eAll DCT components scored above 3.0 in data reliability and privacy domains, with ePRO highest in privacy (item 4 mean\u0026thinsp;=\u0026thinsp;3.10). Remote and physical interventions scored slightly lower (remote 3.02, physical 2.98).\u003c/p\u003e \u003cp\u003e Perceived social benefits were consistently rated highly across all components (mean 3.27\u0026ndash;3.32), with remote COA scoring the highest (3.32), reflecting recognition of DCT\u0026rsquo;s potential to enhance participant diversity and accessibility.\u003c/p\u003e \u003cp\u003eComplexity and communication burden items scored above 3.0 for communication with researchers (item 6) and participants (item 7), but implementation difficulty (item 8) was rated lower for all components, especially remote intervention (2.82), physical intervention (2.87), and remote data collection (2.95), indicating perceived operational challenges.\u003c/p\u003e \u003cp\u003eSatisfaction (item 9) was highest for remote (3.32) and physical interventions (3.31), while economic compensation (item 11) received consistently low scores across all components (mean 2.2\u0026ndash;2.3), suggesting widespread dissatisfaction with financial incentives related to DCT implementation.\u003c/p\u003e \u003cp\u003eKnowledge and system infrastructure items (items 12\u0026ndash;14) scored between 2.5 and 2.8. Remote COA scored lowest in information accessibility (2.66), highlighting the need for infrastructure improvement and guideline development.\u003c/p\u003e \u003cp\u003eFuture outlook items (15\u0026ndash;16) reflected generally positive views on DCT expansion, with ePRO receiving the highest scores for expected future use (item 15 mean\u0026thinsp;=\u0026thinsp;3.33) and anticipated diffusion (item 16 mean\u0026thinsp;=\u0026thinsp;3.26), indicating strong confidence in its scalability and institutional acceptance.\u003c/p\u003e \u003cp\u003ePerception scores for each DCT component across the 16 items are visually summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with detailed means provided in Supplementary Material 2.\u003c/p\u003e\n\u003ch3\u003ePerception Differences by Job Role\u003c/h3\u003e\n\u003cp\u003eTo examine whether DCT perceptions differed by job role (CRC vs. CRA), independent samples t-tests and Mann-Whitney U tests were conducted for the 16 Likert items, accounting for ordinal scale characteristics and potential violations of normality assumptions.\u003c/p\u003e \u003cp\u003eOverall, CRCs reported significantly more favorable perceptions of DCT than CRAs Significant role-based differences were observed across most DCT component. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor remote recruitment, 14 of 16 perception items differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), with CRCs rating feasibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), data protection (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and satisfaction (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) higher than CRAs.\u003c/p\u003e \u003cp\u003eSimilarly, for eICF, 14 items showed significant differences, particularly in feasibility, infrastructure readiness, and ease of use (all p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eIn remote physical intervention, 13 items significantly differed (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), notably in feasibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), satisfaction (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and infrastructure (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with \u0026ldquo;feasibility without site visits\u0026rdquo; being the only nonsignificant item.\u003c/p\u003e \u003cp\u003eFor digital interventions, 12 items differed significantly, with CRCs giving higher ratings on feasibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), ease of use (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), satisfaction (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and information accessibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eIn remote interventions, 11 items showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), mainly in participant communication burden (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), feasibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), satisfaction (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and infrastructure (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eRegarding remote data collection, significant differences were identified in 10 of 16 items, with CRCs consistently reporting higher work efficiency, ease of performance, satisfaction, and information accessibility (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eFor ePRO, 13 items differed significantly between groups across both t-test and Mann\u0026ndash;Whitney analyses, with CRCs showing more favorable perceptions on feasibility, implementation, satisfaction, and readiness (p\u0026thinsp;\u0026lt;\u0026thinsp;.01 to p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eLastly, remote clinical outcome assessments (COA) showed significant differences in 12 items, with CRCs more optimistic about feasibility, implementation, and future use (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), whereas perceptions of social value and privacy concerns were comparable between groups.\u003c/p\u003e \u003cp\u003eThese results consistently indicate that CRCs hold more positive perceptions regarding key DCT components, particularly in operational feasibility, ease of implementation, and infrastructure, reflecting their more active involvement and direct experience with DCT procedures. Detailed means and statistical comparisons by job role are provided in Supplementary Material 3.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePerception Differences by Work Environment\u003c/h2\u003e \u003cp\u003eANOVA was conducted using 16 Likert items to determine perception differences by work environment (clinical trial site, CRO, domestic pharmaceutical/medical device company, multinational pharmaceutical/medical device company). Significant differences were observed across most items and DCT components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor remote recruitment, 15 of 16 items differed significantly by work environment (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), including feasibility, ease of implementation, data reliability, privacy, operational complexity, communication burden, satisfaction, compensation, knowledge, infrastructure, and future expectations. Social value and investigator communication demands did not differ significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;.05). Kruskal-Wallis tests corroborated these findings.\u003c/p\u003e \u003cp\u003eTukey HSD post-hoc analysis revealed that clinical site staff scored significantly higher on most dimensions compared to other groups. For example, site staff rated feasibility significantly higher than CRO staff (items 1 and 2, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Domestic company staff scored higher than multinational company staff on feasibility (item 1, p\u0026thinsp;=\u0026thinsp;.027).\u003c/p\u003e \u003cp\u003eIn data reliability and privacy domains, site experts scored significantly higher than others, and domestic companies scored higher than multinational companies (item 3, p\u0026thinsp;=\u0026thinsp;.0083). Feasibility of implementation (item 8) was superior among site staff versus all other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with domestic companies outperforming multinationals (p\u0026thinsp;=\u0026thinsp;.0004). Similar trends were seen for satisfaction, career utility, and financial incentives.\u003c/p\u003e \u003cp\u003eIn knowledge and infrastructure domains (items 12\u0026ndash;16), site staff showed the most optimism, with domestic companies scoring significantly higher than multinationals (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;=\u0026thinsp;.014).\u003c/p\u003e \u003cp\u003eSimilar patterns emerged for eICF, Direct Delivery to Patient, digital intervention, remote intervention, remote outcome collection, ePRO, and remote clinical outcome assessment, with clinical site staff and domestic company employees consistently reporting more positive perceptions than CRO or multinational company employees. Detailed means and statistical comparisons by job role are provided in Supplementary Material 4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerception Differences by Career Level\u003c/h2\u003e \u003cp\u003eRespondents were categorized into four career experience groups (6\u0026ndash;12 Months, 13\u0026ndash;36 months, 37\u0026ndash;60 months, 61 months or more), and ANOVA and Kruskal-Wallis tests assessed perception differences across the 16 items for each DCT component. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor remote recruitment and enrollment, 14 of 16 items differed significantly by career group (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). The 37\u0026ndash;60 month group consistently showed the highest perception scores across most items, including feasibility, ease of implementation, satisfaction, compensation, and future expectations, while the 61\u0026thinsp;+\u0026thinsp;month group scored the lowest.\u003c/p\u003e \u003cp\u003eSimilar trends were observed for eICF, with significant differences in 15 items; the 37\u0026ndash;60 month group scored higher in time savings, data reliability, and regulatory familiarity, while the 61\u0026thinsp;+\u0026thinsp;month group scored lower.\u003c/p\u003e \u003cp\u003eFor remote physical intervention, 13 items showed significant differences, particularly feasibility, satisfaction, compensation, and future outlook. Detailed means and statistical comparisons by job role are provided in Supplementary Material 5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrioritization of DCT Component Adoption\u003c/h2\u003e \u003cp\u003eA total of 383 respondents were asked to select up to three DCT components they believed should be prioritized for implementation. The most frequently selected item was eICF, receiving 502 selections (43.7%). This indicates a strong consensus regarding the importance of remote consent systems as a foundational tool for participant engagement in decentralized trial settings. The second most selected component was remote enrollment, with 350 responses (30.5%), reflecting widespread interest in improving accessibility and participant convenience. (Supplementary material 6)\u003c/p\u003e \u003cp\u003eDifferences emerged across job roles. While eICF was the top-ranked priority for both CRAs and CRCs, CRAs selected it at a higher rate (27.2% vs. 23.7%). CRAs also prioritized ePRO (17.3%) more than CRCs (7.7%), suggesting a greater emphasis on remote data collection and monitoring.\u003c/p\u003e \u003cp\u003eConversely, CRCs selected remote enrollment at nearly twice the rate of CRAs (22.1% vs. 11.5%), highlighting their focus on direct participant interaction. CRCs also gave higher priority to digital and remote interventions, likely reflecting their operational roles in treatment delivery.\u003c/p\u003e \u003cp\u003eAcross all work environments, eICF was consistently ranked as the top priority: clinical trial sites (24.6%), CROs (25.7%), multinational companies (28.3%), and domestic pharmaceutical companies (30.9%).\u003c/p\u003e \u003cp\u003eSite-based staff also showed high preference for remote enrollment (23.0%) and digital interventions (16.4%). CRO staff favored ePRO (20.4%), likely due to their emphasis on remote monitoring and efficiency. Multinational staff prioritized eICF and remote interventions (28.3%, 17.4%, respectively), while domestic company staff showed stronger preference for digital interventions (22.7%) and eICF (30.9%).\u003c/p\u003e \u003cp\u003eAll career groups identified eICF as the top priority, with selection rates increasing with experience (from 13.5% in the 6\u0026ndash;12 months group to 26.1% in the 61\u0026thinsp;+\u0026thinsp;month group). This trend suggests that senior professionals place greater emphasis on regulatory compliance and procedural integrity.\u003c/p\u003e \u003cp\u003eThe 6\u0026ndash;12 months group favored remote (21.2%) and digital interventions (19.2%), indicating openness to new technologies. Mid-career professionals (13\u0026ndash;60 months) showed a balanced distribution of preferences, with the 37\u0026ndash;60 months group prioritizing digital interventions (19.8%). The most experienced group (61\u0026thinsp;+\u0026thinsp;months) prioritized eICF (26.1%), ePRO (17.7%), and remote interventions (14.8%), reflecting a focus on procedural stability and patient-centered tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Prerequisites for Successful DCT Adoption\u003c/h2\u003e \u003cp\u003eTo identify perceived prerequisites for DCT implementation, respondents were asked to select multiple key enabling factors. The most cited prerequisite was regulatory guidance and approval, chosen by 579 respondents (75.7%), followed by digital tool development (511 responses, 66.9%) and stakeholder training (421 responses, 55.1%). These findings highlight the need for regulatory clarity, technological infrastructure, and human resource preparedness. (Supplementary material 7)\u003c/p\u003e \u003cp\u003eOther notable factors included appropriate compensation for participants (179 responses) and compensation for implementers (176 responses), underscoring the perceived need for fair incentive structures. Ethical concerns were rarely selected (1.6%), suggesting low prioritization relative to structural or operational challenges.\u003c/p\u003e \u003cp\u003eBoth CRAs and CRCs most frequently selected regulatory guidance, tool development, and training. CRAs emphasized training more strongly (23.9% vs. 19.4%), while research nurses prioritized implementer compensation (11.4% vs. 6.4%), reflecting their frontline operational roles.\u003c/p\u003e \u003cp\u003eAll work environments emphasized the same top three prerequisites. Clinical site staff prioritized regulatory guidance (29.4%) and tool development (25.8%), while CRO staff emphasized training (25.3%). Multinational and domestic company staff also prioritized regulatory frameworks (\u0026gt;\u0026thinsp;33%), with domestic company staff placing the highest value on digital tool development (28.4%). .\u003c/p\u003e \u003cp\u003eAll experience levels emphasized regulatory guidance, tool development, and training. Junior professionals (6\u0026ndash;12 months) prioritized ethics and compensation, reflecting a focus on support and protection. Mid-career professionals showed a balanced view, while senior staff (61\u0026thinsp;+\u0026thinsp;months) focused heavily on regulatory and technical infrastructure.\u003c/p\u003e \u003cp\u003eThese results suggest that while the broader consensus emphasizes regulatory and technical readiness, tailored strategies may be needed across job roles, environments, and career levels to support successful DCT adoption.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the acceptance of DCTs among clinical trial professionals in South Korea, using a cross-sectional survey informed by implementation science frameworks. We explored how experience, perception, and attitude toward DCTs vary by job role, organizational environment, and career level, and how these factors interact to shape readiness for DCT adoption.\u003c/p\u003e \u003cp\u003eOur findings highlight that CRCs report significantly greater exposure to DCT components than CRAs, particularly for component involving direct participant interaction. This may reflect the Korean clinical environment, where nurses often manage a broader range of concurrent trials and are more frequently involved in consent and intervention delivery. Mid-career professionals (13\u0026ndash;60 months), particularly those in the 37\u0026ndash;60 months range, demonstrated the highest likelihood of engaging with advanced DCT components. While this finding may be influenced by cumulative exposure, it is notable that their engagement exceeded that of professionals with over 61 months of experience, suggesting that operational roles and openness to innovation may be more influential than tenure alone. However, interpretation should be cautious due to uneven group sizes.\u003c/p\u003e \u003cp\u003ePerception scores reflected optimism toward the feasibility and benefits of DCTs, especially for digital tools like eConsent and ePRO. Nonetheless, concerns about regulatory clarity, data protection, and compensation remain. CRCs and mid-career professionals expressed significantly more favorable perceptions than their counterparts, and regression models showed that prior experience was a strong predictor of positive attitudes\u0026mdash;especially for implementation ease, satisfaction, and information access.\u003c/p\u003e \u003cp\u003eIn line with the PRECEDE\u0026ndash;PROCEED model, it was identified key behavioral and environmental determinants of DCT adoption. Predisposing factors included differences in knowledge and beliefs by role and experience; enabling factors included regulatory guidance, digital tool development, and stakeholder education; and reinforcing factors were strongly linked to hands-on experience. It also was found evidence of resistance mechanisms, including psychological resistance among senior staff and functional resistance related to implementation complexity and misalignment with existing roles.\u003c/p\u003e \u003cp\u003eThese results support a phased implementation approach that begins with low-barrier DCT components, offers experiential learning opportunities, and incorporates tailored support by job function and career level. Mid-career professionals\u0026mdash;particularly those with 13\u0026ndash;36 months of experience\u0026mdash;emerged as key targets for institutional diffusion strategies. For high-complexity components, simulation training, case archives, and pilot trials may mitigate resistance and build operational confidence.\u003c/p\u003e \u003cp\u003eImportantly, while the study focused on South Korea, its insights have global relevance. The structural and behavioral dynamics observed are likely to apply in other hybrid or transition-phase health systems, positioning this research as a valuable reference for international DCT policy development.\u003c/p\u003e \u003cp\u003eAlthough the survey assessed cumulative experience, and comparisons between lower and mid-level groups should be interpreted cautiously, it is notable that the mid-career group outperformed even the most experienced professionals. Sampling imbalances between career groups should also be considered.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a multidimensional assessment of the Korean clinical trial workforce\u0026rsquo;s readiness for DCT implementation, elucidating the influence of job role, work environment, experience, and exposure on preparedness and resistance. The adoption of DCT is influenced not only by technical availability but also by institutional, behavioral, and experiential alignment.\u003c/p\u003e \u003cp\u003eFindings highlight the necessity of role- and phase-specific training, infrastructure investment, and experience-based implementation strategies. Policymakers and organizational leaders should prioritize regulatory clarity, tool development, and capacity building to ensure sustainable and equitable DCT integration.\u003c/p\u003e \u003cp\u003eLimitations include potential self-selection bias, unequal subgroup sizes, and sampling from specific study sites; however, the study offers a robust empirical foundation to enhance DCT readiness globally, including in Korea. Future research should expand to include sponsor stakeholders, trial designers, and longitudinal tracking of evolving perceptions and implementation outcomes.\u003c/p\u003e \u003cp\u003eOverall, this evidence-based, practice-oriented policy roadmap underscores the importance of addressing real-world operational contexts to strategically expand decentralized clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was\u0026nbsp;supported by the SmartTech Clinical Research Center (SCRC), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (grant number :\u0026nbsp;RS-2023-KH142023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2025-01-069 ). Online informed consent was obtained from all participants prior to data collection, and all responses were anonymized. The study adhered to ethical standards, ensuring voluntary participation, confidentiality, and data protection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Seon Ji, Juhee Cho\u003c/p\u003e\n\u003cp\u003eMethodology: Seon Ji, Juhee Cho, Mangyeong Lee\u003c/p\u003e\n\u003cp\u003eFormal analysis: Seon Ji\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Seon Ji\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: Seon Ji, Juhee Cho, Man Gyeong Lee, Hae Sook Bok, Su Jin Kim\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupervision: Juhee Cho\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnderhill C, Freeman J, Dixon J, et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.konect.or.kr/kr/board/konect_library_01/boardList.do\u003c/span\u003e\u003cspan address=\"https://www.konect.or.kr/kr/board/konect_library_01/boardList.do\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical Trial, Decentralized Clinical Trial, DCT","lastPublishedDoi":"10.21203/rs.3.rs-8416486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8416486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDecentralized clinical trial (DCT)s have emerged as a promising approach to increase access, efficiency, and patient-centricity in clinical research. Although global regulatory bodies have encouraged the adoption of digital trial components such as electronic consent and remote monitoring, empirical evidence on workforce readiness and acceptance remains limited.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate experience, perceptions, and attitudes toward DCTs among South Korean clinical trial professionals.\u003c/p\u003e \u003cp\u003eA cross-sectional survey was conducted with 383 clinical research professionals, including clinical research coordinator (CRC)s and clinical research associates (CRA)s. The questionnaire measured experience with eight core DCT component and assessed perceptions across seven domains, including feasibility, satisfaction, and outlook. Group differences were analyzed based on job role, organizational affiliation, career level, and prior DCT experience. Statistical analyses included Kruskal\u0026ndash;Wallis, ANOVA, logistic regression, and linear regression.\u003c/p\u003e \u003cp\u003eThe most experienced DCT components were electronic informed consent (43.1%), remote enrollment (44.4%), and ePRO (43.1%). CRCs and mid-career professionals (13\u0026ndash;60 months) reported higher engagement and more favorable perceptions compared to other groups. Regression analyses showed that prior DCT experience significantly predicted higher scores in perceived ease of implementation, satisfaction, and expansion expectations (β\u0026thinsp;\u0026gt;\u0026thinsp;0.5).\u003c/p\u003e \u003cp\u003eThese findings underscore that DCT adoption is not solely a technical issue but depends on organizational context, job role, and experiential learning. Mid-career professionals represent a critical target group for capacity-building. Although the study focused on South Korea, the results may inform global efforts to design scalable and context-sensitive DCT implementation strategies.\u003c/p\u003e","manuscriptTitle":"Experience, Perception, and Attitudes Toward Decentralized Clinical Trial (DCT) Among Clinical Research Coordinators and Clinical Research Associates in South Korea: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 01:35:19","doi":"10.21203/rs.3.rs-8416486/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"30353265949838197746202927369764409238","date":"2026-02-03T01:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T21:25:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T08:06:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T08:04:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trials","date":"2025-12-21T09:51:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94690a5b-e037-4196-929c-f441b198ba75","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T01:35:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 01:35:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8416486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8416486","identity":"rs-8416486","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}