Effectiveness of Virtual Nurse Interventions in Chronic Disease Self-Management: A Systematic Review of Randomized Controlled Trials

Effectiveness of Virtual Nurse Interventions in Chronic Disease Self-Management: A Systematic Review of Randomized Controlled Trials | 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 Effectiveness of Virtual Nurse Interventions in Chronic Disease Self-Management: A Systematic Review of Randomized Controlled Trials Chang Tsuei-Wun, Hsin-Yi Chen, Yu-Chi CHEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8182311/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in BMC Nursing → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Chronic diseases require ongoing self-management, yet patients often struggle with knowledge, behavioral adherence, and emotional well-being. Virtual nurse interventions, digital systems simulating nurse–patient interactions, offer a potential strategy to support self-management. This review aimed to evaluate their effectiveness in improving health knowledge, behavioral outcomes, psychological well-being, and patient satisfaction. Methods: A systematic search of the Cochrane Library, PubMed, CINAHL, MEDLINE, and EMBASE was conducted for randomized controlled trials published between 2014 and April 2025. Studies involving adults with chronic diseases and virtual nurse interventions reporting outcomes on knowledge, behavior, psychological well-being, or patient satisfaction were included. Methodological quality was appraised using the Joanna Briggs Institute checklist. Due to heterogeneity in interventions and outcome measures, data were synthesized narratively. Results: Six trials with 533 participants were included. Virtual nurse interventions consistently improved disease-specific knowledge and psychological well-being, and were associated with high patient satisfaction. Evidence for behavioral adherence and clinical outcomes was limited and often not statistically significant. Conclusions: Virtual nursing enhances knowledge, emotional support, and psychological well-being in chronic disease self-management. Its long-term effects on behavioral change and clinical outcomes remain unclear. Nurse engagement, organizational support, and attention to digital literacy are essential to optimize implementation. Trial registration: PROSPERO CRD420251038439. Virtual nurse. Chronic disease. Self-management. Patient education. Patient satisfaction Figures Figure 1 1. Introduction Chronic diseases remain the leading cause of mortality and disability worldwide, imposing substantial burdens on patients, families, and healthcare systems (Bennett et al., 2018 ; Hacker, 2024 ). The aging global population has increased the prevalence of conditions such as diabetes, hypertension, and chronic kidney disease, all of which require continuous self-management and long-term support (Hacker, 2024 ; Jiang et al., 2024 ). This rising health burden highlights the need for scalable, effective, and sustainable care models applicable across diverse healthcare systems (Tao et al., 2023 ; Bin Sawad et al., 2022 ). Patient education is fundamental to chronic disease management, empowering individuals with the knowledge and skills necessary for self-care, adherence to treatment regimens, and early detection of potential complications, thereby improving health outcomes and quality of life (Correia et al., 2023 ; Jiang et al., 2024 ). Nurses play a central role in delivering this education; however, traditional methods—such as face-to-face sessions and printed materials—are often constrained by limited time, workforce shortages, and lack of individualized tailoring (Acharya et al., 2024 ; Giguère et al., 2020 ). These challenges are particularly pronounced for patients requiring continuous, personalized support in home-based care environments. Digital health innovations, particularly AI-driven technologies, have emerged as promising adjuncts to traditional nursing care (Al Kuwaiti et al., 2023 ; Wonggom et al., 2020 ). Virtual nurses—AI-based agents simulating nurse–patient interactions—provide personalized education, medication reminders, symptom monitoring, and emotional support through mobile apps, web platforms, and virtual reality (Milne-Ives et al., 2020 ; Gong et al., 2020 ). Unlike generic chatbots, virtual nurses incorporate anthropomorphic features such as facial expressions, vocal intonation, emotional recognition, and adaptive feedback, fostering more engaging and empathetic interactions (Aggarwal et al., 2023 ; Jiang et al., 2024 ). Evidence suggests these interventions can enhance patient motivation, health literacy, and behavior change, particularly in home-based or remote care settings (Galvão et al., 2018 ; Chattopadhyay et al., 2020 ; Bin Sawad et al., 2022 ; Zhang et al., 2025 ). Challenges remain, including barriers for patients with limited digital literacy, especially older adults or socioeconomically disadvantaged groups (Arcury et al., 2020 ; Cheng et al., 2022 ), and ethical considerations such as data privacy, algorithmic bias, informed consent, and transparency (Al Kuwaiti et al., 2023 ; Graham-Brown et al., 2023 ). These issues underscore the importance of inclusive design and rigorous evaluation to ensure safety, efficacy, and equity in clinical practice. Although previous systematic reviews have examined chatbots and AI applications in healthcare, few have specifically evaluated the clinical effectiveness of anthropomorphic virtual nurses in chronic disease self-management. Moreover, the impact of design factors—such as interaction frequency, delivery modality, and personalization—remains underexplored. Therefore, this systematic review aims to evaluate the effectiveness of virtual nurse interventions in supporting chronic disease self-management, focusing on health knowledge, behavioral outcomes, psychological well-being, and patient satisfaction, while examining design characteristics, user experience, and implementation considerations. 2. Methods 2.1 Study Protocol This systematic review adhered to the Synthesis Without Meta-analysis (SWiM) (Campbell et al., 2020 ) due to the heterogeneity of intervention designs and outcome measures (Table S1 ). Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021 ). The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). 2.2 Design The review was structured around the following PICO framework: Among adults with chronic diseases (Population), do virtual nurse interventions (Intervention), compared to usual care or alternative educational interventions (Comparison), improve outcomes (Outcome), specifically disease-related knowledge, self-management behaviors, psychological well-being, physiological indicators, patient satisfaction, and acceptability? The study population included adults aged 18 years or older diagnosed with chronic conditions such as diabetes, heart failure, or atrial fibrillation. The interventions encompassed virtual nursing systems featuring avatars, relational agents, or AI-based digital characters exhibiting human-like attributes (e.g., facial expressions, voice modulation, and emotional responsiveness). The comparison group consisted of usual care or other educational interventions delivered by healthcare professionals or digital platforms. The primary outcome focused on improvement in disease-related knowledge and awareness, while secondary outcomes included self-management behaviors, physiological indicators, psychological well-being, patient satisfaction, and acceptability. Eligible studies for inclusion were randomized controlled trials assessing virtual nursing systems designed to facilitate patient education, coaching, or chronic disease self-management. The synthesis compared intervention delivery formats, outcome domains, and patient-reported measures to ascertain overall effectiveness. 2.3 Search Method A comprehensive literature search was conducted using five electronic databases: the Cochrane Library, PubMed, CINAHL, MEDLINE, and EMBASE. The search covered studies published between January 2014 and April 2025 and was limited to peer-reviewed articles published in English and Chinese. Keywords and Medical Subject Headings (MeSH) were derived from the PICO framework and included: “virtual nurse,” “avatar,” “relational agent,” “embodied conversational agent,” “digital health,” “self-management,” “chronic disease,” and “patient education.” Boolean operators (“AND” and “OR”) were used to optimize both the sensitivity and specificity of the search strategy. The detailed search strategies for each database are provided in Table S2. All studies identified for inclusion were cross-referenced with those included in a larger systematic review by the authors (unpublished) of all risk assessment scales. Additionally, the reference lists of systematic reviews found in the search were screened to identify potential articles for inclusion. 2.4 Inclusion and/or Exclusion Criteria The inclusion criteria were: (1) RCTs; (2) adult participants (≥ 18 years) diagnosed with chronic diseases; (3) interventions involving virtual nurse or avatar-based systems; and (4) reporting of relevant outcomes related to patient education or self-management. Reviews, protocols, conference abstracts, clinical guidelines, and studies without outcome data were excluded. The reference lists of all the included studies were manually screened for additional relevant articles. When full-text articles were not accessible, the corresponding authors were contacted. EndNote reference software was used for article screening and data extraction. Duplicates were removed and two reviewers independently assessed the study titles and abstracts to identify potentially relevant studies that met the inclusion criteria. Those who did not meet the inclusion criteria were excluded and their reasons were recorded. Following the title and abstract screening, full-text screening was performed independently by two reviewers to assess eligibility. Any disagreements were discussed and resolved by a third reviewer, as necessary. 2.5 Search Outcome A total of 54 unique citations were initially identified through database searches. Following screening, eight articles were selected for full-text eligibility assessment. Upon detailed review, two articles were excluded: one because it was not a RCT, and the other for lacking relevant outcome data. Thus, six studies were included in the final analysis. The study selection process is illustrated in the PRISMA flowchart shown in Fig. 1 . 2.6 Quality Appraisal The methodological quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Randomized Controlled Trials (2020). The checklist consists of 13 items, each scored as “Yes” (1 point), “No,” “Unclear,” or “Not applicable” (0 points), with a maximum score of 13. Studies with scores > 7 were considered methodologically adequate (Moola et al., 2020 ). Two independent reviewers performed the evaluations. Any disagreements were resolved through discussion and consultation with a third reviewer when consensus could not be reached. 2.7 Data Extraction and Synthesis Data were independently extracted and summarized using a standardized template. The extracted information included author(s), publication year, country, target population, disease type, sample size, description of the virtual nurse intervention, delivery platform, and measured outcomes. Two reviewers independently conducted the data extraction. Disagreements regarding the extracted data were resolved through discussion between the reviewers, and, where needed, a third reviewer was consulted to resolve discrepancies. Due to the heterogeneity in intervention designs, durations, and assessment tools, a meta-analysis was not appropriate. Synthesis without meta-analysis was conducted to categorize the interventions and summarize the outcomes across five domains: health knowledge, behavioral changes, physiological indicators, psychological well-being, and patient satisfaction. 3. Results 3.1 Characteristics of Included Studies A summary of these studies is presented in Table 1 , which details disease focus, delivery platforms, and outcome domains. All six included studies were RCTs conducted between 2014 and 2022, primarily in the United States or Australia. A total of 533 participants were enrolled across the studies, all of whom were adults (≥ 18 years) diagnosed with chronic conditions that included type 2 diabetes mellitus, heart failure, atrial fibrillation, or acute coronary syndrome. Table 1 Summary of Included Studies on Virtual Nurse Interventions (N = 6) First author (Year) Country Participants Sample Size Disease Type Type and Interface of Virtual Nurse Educational Content Duration Outcomes Rosal et al. ( 2014 ) USA Non-Hispanic Black women Sex: all female Mean age: 52 years 89 Type 2 Diabetes Virtual world (Second Life), group-based interactive avatars Diet, exercise, self-monitoring 8 weeks (90 min/week) HbA1c decreased by 3.2% in the virtual group and 4.9% in the in-person group (p = .52). Physical activity increased by 18.4% in the virtual group (p = .52). Depression improved more in the in-person group (p = .051). Satisfaction : 97% preferred face-to-face vs. 80% preferred virtual (p = .490); 100% vs. 97% would recommend (p = 1.0). Medication adherence decreased by 8.6% in the face-to-face group and increased by 1.2% in the virtual group; no significant between-group difference (p = .298). Blood Pressure : Systolic BP remained unchanged in the face-to-face group and increased by 1.5% in the virtual group; diastolic BP decreased by 2.0% and increased by 0.9%, respectively; no significant between-group differences (systolic BP p = .609; diastolic BP p = .675). Self-management : improved in both groups (p < .001) with no between-group difference (p = .268). Magnani et al. ( 2017 ) USA Patients with atrial fibrillation Sex: 19 males, 12 females Mean age: 68 years 31 Atrial Fibrillation computer-animated relational agent (Tanya) AF education, symptoms, medication adherence, self-management strategies 30 days (average 17.8 days of use) AFEQT scores : improved from 64.5 (baseline) to 76.3 at 30 days (p < .01). MMAS-8 : increased from 7.3 (baseline) to 7.7 at 30 days (p = .01). PAM : increased from 3.0 (baseline) to 3.4 at 30 days (p = .33). Acceptability : most found agent useful and trustworthy. Tongpeth et al. ( 2020 ) Australia Acute Coronary Syndrome patients Sex: 44 males, 26 females Mean age: 64.7 years 70 Acute Coronary Syndrome Tablet-based avatar app (Nurse Cora) with animation and voice interaction ACS symptoms, action plan, knowledge/attitude education 6 months (mean review average 6 per person) ACS knowledge increased from 58.35% to 83.55% (p < .001). Symptom knowledge, attitudes, and beliefs improved in the intervention group (p < .001, .009, .001). ACS Response Index scores were higher at 1 and 6 months (p < .01). Ambulance use was greater in the intervention group (33.3% vs. 18.2%, p = .008). Confidence : 85% reported feeling more confident recognizing and responding to symptoms. Satisfaction : all 33 participants were satisfied with the Avatar app. Gong et al. ( 2020 ) Australia Adults with T2DM Sex: 109 males, 78 females Mean age: 56.9 years 187 Type 2 Diabetes Mobile app (MDC) with virtual agent "Laura" Blood glucose management, lifestyle coaching, medication education 12 months (average 243 min/person) Engagement : participants completed 1942 chats. HbA1c : decreased by 0.33% in the intervention group and 0.20% in the control group; no significant between-group difference (− 0.04%, 95% CI − 0.45 to 0.36, p = .83). HRQoL : utility scores improved more in the intervention arm (between-arm difference 0.04, 95% CI 0.00 to 0.07, p = .04). HADS anxiety score : significant between-arm difference in mean change at 6 months (− 0.89, 95% CI − 1.74 to − 0.04, p = .04). Guhl et al. ( 2020 ) USA Patients with atrial fibrillation Sex: 58 males, 62 females Mean age: 72.1 years 120 Atrial Fibrillation Smartphone-based virtual nurse "Tanya" + HR monitoring Emotional support, quality of life enhancement 30 days (average 40.7 min ) AFEQT scores : higher in the intervention group (total 4.5, 95% CI 0.6 to 8.3, p = .03). Anticoagulant adherence : better self-reported adherence at 30 days (3.5% vs. 23.2%, adjusted difference 16.6%, 95% CI 2.8% to 30.4%, p < .001). Acceptability : most found the agent useful, informative, and trustworthy; median satisfaction 6/7, easy to use (median 1), content somewhat repetitive (median 5). Wonggom et al. ( 2020 ) Australia Patients with heart failure Sex: 29 males, 7 females Mean age: 67.5 years 36 Heart Failure Tablet-based avatar app Personalized health advice and lifestyle support 90 days (2.6–4 times/month) Knowledge : intervention group had greater improvement in HF knowledge at 90 days (22.2% vs. 3.7%, p = .002), with a smaller non-significant difference at 30 days (15.3% vs. 3.5%, p = .061). Self-care : improved only in the intervention group at 30 days (11.9% vs. −4.3%) and 90 days (17.3% vs. −2.9%, p = .244). Healthcare use : no significant differences in hospital readmissions at 30 or 90 days (p > .3). Satisfaction : overall satisfaction with the avatar app was high (91.3%), with most finding it easy to understand (100%), concise and easy to follow (95%). Note: T2DM = Type 2 Diabetes Mellitus; ACS = Acute Coronary Syndrome; AF = Atrial Fibrillation; HF = Heart Failure; NST = Not statistically tested; AFEQT = Atrial Fibrillation Effect on Quality-of-Life questionnaire; MMAS-8 = 8-Item Morisky Medication Adherence Scale; PAM = Patient Activation Measure; HRQoL = Health-Related Quality of Life; HADS = Hospital Anxiety and Depression Scale; BP = Blood Pressure. Virtual nurse interventions vary in design and platform, encompassing embodied avatars, relational agents, and AI-driven digital characteristics. These interventions were delivered in a range of formats, including tablet-based applications (e.g., Nurse Cora and Tanya), mobile health applications (e.g., My Diabetes Coach), virtual reality platforms (e.g., Second Life), and web-based systems. Interventions targeted key domains, such as disease-specific knowledge, self-management support, emotional well-being, and health behavior change, with durations ranging from 30 days to 12 months. The outcomes assessed included HbA1c levels, medication adherence, quality of life, health knowledge, psychological indicators (e.g., depression and anxiety), and user satisfaction. Most studies reported positive effects in at least one domain, particularly on improvements in health knowledge, quality of life, and patient satisfaction. A summary of the included studies, including sample characteristics, virtual nurse formats, and key findings, is presented in Table 1 . A comparison of the outcome domains is presented in Table 2 . Table 2 Comparison of Outcomes Across Included Studies Author (Year) Disease Type Knowledge Improvement Behavioural change Physiological Psychological Quality of Life User Satisfaction Physical Activity Behavioral Response Medication Adherence Self-management Glycemic (HbA1C) Blood Pressure Depression Symptoms Rosal et al. ( 2014 ) T2DM - ↑ - ↑ ↑ ↑ ↑ ↑ - ↑ Magnani et al. ( 2017 ) AF - - - + ↑ - - - + ↑ Tongpeth et al. ( 2020 ) ACS + - + - - - - - - ↑ Gong et al. ( 2020 ) T2DM - - - - - ↑ - + + ↑ Guhl et al. ( 2020 ) AF - - - + - - - - + ↑ Wonggom et al. ( 2020 ) HF + - - - ↑ - - - - ↑ Note: ↑ = Positive change (not statistically significant) ; + = Statistically significant improvement ; –= Not reported.「 For details, see Section 3.5 」 Abbreviations: T2DM = Type 2 Diabetes Mellitus; ACS = Acute Coronary Syndrome; AF = Atrial Fibrillation; HF = Heart Failure; NST = Not statistically tested Due to the substantial heterogeneity in intervention modalities, outcome measures, follow-up durations, and evaluation tools, a meta-analysis was not feasible. Therefore, the findings were synthesized using a narrative approach. 3.2 Quality of Studies The methodological quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Randomized Controlled Trials (2020). As shown in Table S3, all six studies received scores ranging from 9 to 10 out of a maximum of 13, indicating moderate-to-high methodological quality. All studies clearly described their randomization procedures and participant characteristics, and most demonstrated baseline comparability, consistent intervention delivery, the use of reliable outcome measures, and appropriate statistical analyses. However, several methodological limitations were noted. Specifically, allocation concealment and the blinding of participants and outcome assessors were either unreported or not feasible, largely because of the interactive and visually distinguishable nature of virtual nurse interventions. 3.3. Technology Formats and Functions of Virtual Nurses All included studies assessed the participants’ digital literacy or device access prior to enrollment to ensure the feasibility of using virtual nurse technologies. Overall, the interventions were well received, and they demonstrated positive effects on health knowledge, self-management behaviors, and selected physiological and psychological outcomes (Table 2 ). The technological formats and functional characteristics of virtual nursing systems vary along two primary dimensions: 3.3. 1. Interaction Modes and Interface Types Virtual nurse interventions are delivered via mobile apps, tablets, websites, or virtual reality environments. Most systems employ human-like avatars that interact through text-, voice-, or video-based communication to simulate natural nurse–patient conversations. For example, Rosal et al. ( 2014 ) implemented a virtual-world group education model using Second Life; Gong et al. ( 2020 ) utilized a voice-based agent named Laura to deliver diabetes education; Magnani et al. ( 2017 ) employed a computer-animated relational agent named Tanya to provide atrial fibrillation education; and Wonggom et al. ( 2020 ) developed a tablet-based avatar application to educate patients with heart failure (Table 1 ). 3.3.2. Educational Content, Duration, and Delivery Flexibility The educational content was aligned with evidence-based clinical guidelines and focused on core self-management areas, including symptom monitoring, medication adherence, dietary and lifestyle modifications, and emotional well-being. Interventions incorporated multimedia elements, such as videos, infographics, and interactive quizzes. The study duration ranged from 30 days to 12 months, with interaction frequencies varying from daily to weekly. Several systems have enabled self-paced learning and provided adaptive feedback based on user input, thereby enhancing personalization and flexibility. 3.4 Outcome Indicators Outcomes were synthesized narratively across five key domains: health knowledge, self-management behaviors, physiological indicators, psychological wellbeing, and user satisfaction. A cross-study comparison of the outcomes is summarized in Table 2 . 3.4.1 Health Knowledge and Disease Awareness Significant improvements in the disease-specific knowledge were observed in the included studies. Tongpeth et al. ( 2020 ) reported marked increases in symptom recognition among patients with acute coronary syndrome, rising from 58.35% to 83.55%, along with gains in symptom knowledge (p < .001), attitudes (p = .009), and beliefs (p = .001). Acute Coronary Syndrome Response Index scores remained elevated at both 1 and 6 months (p < .01), with 85% of the participants expressing greater confidence in recognizing and responding to symptoms. Wonggom et al. ( 2020 ) demonstrated improved heart failure knowledge, with a 22.2% increase in the intervention group compared to 3.7% in the control group (p = .002). Gong et al. ( 2020 ) did not report direct knowledge scores but showed high user engagement, recording 1,942 chatbot sessions, suggesting effective educational delivery. (See Table 1 for an outcome summary of the studies). 3.4.2 Self-management and Behavioral Change Positive behavioral outcomes have been reported. Rosal et al. ( 2014 ) found an 18.4% increase in physical activity in a virtual group compared to a 22.5% decrease in a face-to-face group (p = .10). Both groups showed improvement in diabetes self-efficacy (p < .001), with no significant difference between them (p = .268). Medication adherence outcomes were similarly favorable. Guhl et al. ( 2020 ) identified higher self-reported anticoagulant adherence at 30 days in the intervention group vs. control, with an adjusted difference of 16.6% (p < .001). Magnani et al. ( 2017 ) found that MMAS-8 adherence scores increased from 7.3 to 7.7 over 30 days (p = .01), although patient activation measured by PAM remained unchanged (p = .33). Wonggom et al. ( 2020 ) reported improved self-care in the intervention group at both 30 and 90 days with no significant differences in hospital readmissions. (See Table 2 for a comparative summary of behavioral changes). 3.4.3 Physiological Outcomes Modest reductions in HbA1c levels were observed in patients with diabetes. Rosal et al. ( 2014 ) reported a 3.2% reduction in the virtual group (p = .186) and a significant 4.9% decrease in the face-to-face group (p = .019) with no significant differences between the groups (p = .52). Gong et al. ( 2020 ) noted a modest reduction of 0.33% in the intervention group compared with 0.20% in the control group (p = .83). Regarding blood pressure, Rosal et al. ( 2014 ) found no significant changes over time. In the face-to-face group, systolic BP remained at 126.0 mmHg (p = .808) with a slight decrease in diastolic BP (p = .733). The virtual group showed slight increases in systolic and diastolic BP (p = .233 and p = .600, respectively). Between-group differences were not statistically significant (p > .60). (See Table 2 for the glycemic and blood pressure outcomes). 3.4.4 Psychological Wellbeing and Emotional Support Virtual nursing systems have been shown to provide psychological benefits. Gong et al. ( 2020 ) reported significant reductions in anxiety and depression with greater improvements in health-related quality of life utility scores in the intervention group (p = .04). HADS anxiety scores similarly favored the intervention group at six months (p = .04). Rosal et al. ( 2014 ) observed reduced depressive symptoms in both groups, with marginally greater improvements in the face-to-face group (p = .051). Notably, these psychological outcomes were accompanied by quality of life gains. Guhl et al. ( 2020 ) reported higher AFEQT scores in patients receiving virtual interventions (p = .03), and Magnani et al. ( 2017 ) found that scores increased from 64.5 to 76.3 (p < .01). These findings suggest that virtual nursing systems may provide psychological support, in addition to educational benefits (see Table 2 for depression and quality of life outcomes). 3.4.5 Patient Satisfaction and Acceptability User satisfaction with the virtual nursing system was consistently high. Guhl et al. ( 2020 ) reported that most participants found the virtual agent useful, informative, and trustworthy (median satisfaction 6/7), with the tool rated easy to use, though some noted that the content was repetitive. Wonggom et al. ( 2020 ) observed an overall satisfaction rate of 91.3%, with nearly all participants finding the app clear and easy to follow. Magnani et al. ( 2017 ) highlighted the positive perceptions of daily monitoring, and Tongpeth et al. ( 2020 ) found that all participants were satisfied with an avatar app. However, several studies have identified technical usability issues, such as interface complexity or limited interactivity, suggesting the need for improved interface design and user-centered functionality in future development. (See Table 2 for detailed satisfaction outcomes.) 4. Discussion This systematic review assessed the effectiveness of anthropomorphic virtual nurse interventions in supporting chronic disease self-management, with particular focus on improvements in health knowledge, self-care behaviors, psychological well-being, and user satisfaction. Overall, the evidence suggests that virtual nursing interventions are feasible, acceptable, and beneficial in enhancing patient education and engagement. Notably, consistent improvements were observed in disease-specific knowledge and patient satisfaction, underscoring the potential of these systems as valuable educational tools. However, changes in clinical outcomes—such as glycated hemoglobin (HbA1c) levels and blood pressure—were generally modest and frequently lacked statistical significance, indicating that achieving meaningful physiological improvements may require prolonged or more intensive intervention. 4.1 Educational Functions and Delivery Models Virtual nursing systems simulate nurse–patient interactions through personalized education, behavioral coaching, and emotional support. In this review, most systems employed humanlike avatars capable of voice-, text-, or video-based interactions, enabling real-time or asynchronous communication. Wonggom et al. ( 2020 ) and Tongpeth et al. ( 2020 ) demonstrated significant improvements in disease-specific knowledge and symptom recognition, highlighting the value of interactive and multimodal learning environments. These findings align with prior studies showing that repetitive, self-paced digital education enhances health literacy and retention (Chattopadhyay et al., 2020 ). Additionally, virtual agents using narrative dialogues and simulated scenarios, as seen in relational agents, may foster emotional relevance and deeper understanding (Correia et al., 2023 ; Giguère et al., 2020 ). In particular, systems that integrate narrative dialogues or simulated scenarios, as seen in relational agents, may increase emotional relevance and deepen our understanding (Chattopadhyay et al., 2020 ; Galvão et al. 2018 ; Jiang et al., 2024 ). Similar to eHealth interventions in oncology and nephrology settings, the format and frequency of virtual nurse interactions vary across studies, ranging from daily app-based monitoring (Gong et al., 2020 ) to weekly education sessions in virtual group classes (Rosal et al., 2014 ). As Flythe et al. ( 2020 ) noted, in tablet-based chronic kidney disease monitoring, the optimal usage frequency must balance engagement and burden to prevent dropouts. 4.2 Design Challenges and Considerations Although virtual nursing systems have strong educational potential, their design and implementation present notable challenges. A key issue lies in the lack of standardization across interventions, particularly in terms of content scope, interaction frequency, and feedback mechanisms, which hampers the comparability and generalizability of the findings. Although some studies have employed high-fidelity relational agents and sophisticated interfaces, many systems have failed to incorporate adaptive features that tailor educational content based on patient needs, responses, or digital literacy levels. Designing an effective system requires a balance between technological complexity and user accessibility. As highlighted by Graham-Brown et al. ( 2023 ), involving target users early in the co-design process can help ensure that content remains clinically appropriate, interfaces are intuitive, and user burden is minimized. Such participatory approaches are critical for creating virtual nurse interventions that are both person-centered and sustainable in real-world settings. Another critical yet often-overlooked factor is digital health literacy, which significantly influences the ability of patients to engage with virtual nurse technologies. Most of the included studies did not assess or report on the digital competence of participants, limiting insights into how user readiness may have affected the outcomes. As noted by Arcury et al. ( 2020 ) and Estrela et al. ( 2023 ), older adults and socioeconomically disadvantaged populations are more likely to face challenges in using digital health tools because of limited access, skills, or confidence. Virtual nurse systems should incorporate multilingual support, onboarding assistance, and intuitive low-burden interfaces. Furthermore, offering structured digital health literacy training, either prior to or alongside intervention delivery, can help reduce disparities in uptake and improve the effectiveness of these tools among marginalized groups (Bui et al., 2025 ; Cheng et al., 2022 ; Zhang et al., 2024 ). Addressing these user-level barriers is essential to ensuring that virtual nursing interventions do not inadvertently widen the digital divide. Further design considerations involve aligning educational content with clinical practice guidelines. As shown by Acharya et al. ( 2024 ), AI-generated health education materials, including those integrated into virtual nurse systems, may deviate from established clinical standards, such as evidence-based chronic kidney disease guidelines. This raises concerns regarding the accuracy, safety, and credibility of the information delivered to patients. To mitigate these risks, nurses and clinical experts must remain actively involved in the development, validation, and quality assurance of the content. Such oversight ensures that virtual nurse interventions not only meet educational goals, but also uphold clinical integrity and patient safety. 4.3 Clinical Integration and Population Considerations Virtual nursing technologies have the potential to support care in both home and outpatient settings, particularly in contexts where access to in-person nursing education is limited. The consistently high levels of user satisfaction observed across the studies in this review underscore their acceptability among community-dwelling adults and suggest the feasibility of a broader implementation. However, several population-specific challenges have hindered its widespread adoption. Older adults or individuals with limited digital health literacy may face barriers such as unfamiliarity with technology, inconsistent Internet access, or physical limitations affecting device use (Bui, et al., 2025 ; Cheng et al., 2022 ; Zhang et al., 2025 ). In addition, nurses may be reluctant to integrate digital tools into practice because of insufficient training, workflow constraints, or a lack of institutional support (Anderson et al., 2021; Flythe et al., 2020 ). To maximize their clinical impact, virtual nursing systems should be integrated into routine workflows, including discharge planning, chronic disease management programs, and telehealth services. Embedding these tools in existing care structures can enhance their continuity and relevance. Furthermore, collaborative implementation models—involving nurses in system co-design, user testing, and outcome evaluation—can improve usability, trust, and adoption (Galvão et al., 2018 ; Graham-Brown et al., 2023 ). Finally, institutional investments in infrastructure, technical support, and continuing education for healthcare providers are essential. Equipping nurses with the competencies to guide patients through virtual care systems is critical for a successful and sustainable integration into chronic disease care (Anderson et al., 2021). 4.4 Effectiveness Variability and Future Research While this review identified consistent improvements in knowledge, psychological well-being, and user satisfaction, the effects on behavioral changes and physiological outcomes such as HbA1c and blood pressure were less conclusive. For example, Gong et al. ( 2020 ) and Rosal et al. ( 2014 ) observed that reductions in HbA1c fell below thresholds for clinical relevance, as summarized in Table 2 under “Physiological Outcomes,” among participants using virtual nurse interventions. This aligns with the broader eHealth literature, which consistently shows that knowledge acquisition alone rarely leads to sustained learning without motivational, social, and environmental reinforcement (Aggarwal et al., 2023 Maguire et al., 2021 ; Milne-Ives et al., 2020 ). To advance this field, future research should prioritize long-term RCTs with extended follow-ups to evaluate the sustained impact of virtual nurse interventions on behavioral adherence and clinical outcomes. Comparative trials exploring different interaction modalities (e.g., video vs. text-based agents) and blended models (human + virtual) that combine human and virtual support could help identify optimal delivery strategies. Moreover, incorporating assessments of digital health literacy is essential for understanding how user characteristics influence intervention effectiveness, as well as for guiding the tailoring of design elements that can reduce disparities in use and outcomes. Additionally, future studies should examine how tailored design elements such as adaptive feedback, culturally appropriate content, and emotionally responsive interfaces can reduce inequities in access and outcomes across diverse patient populations. Exploring these customization factors is critical to enhancing both engagement and clinical utility. Finally, the evaluation of cost-effectiveness and scalability is vital to determining the broader impact of virtual nurse programs. Although initial development and implementation may require investment, evidence suggests that well-designed systems can help reduce hospital readmissions, improve self-care adherence, and alleviate the burden on healthcare personnel (Milne-Ives et al., 2020 ; Bin Sawad et al., 2022 ). Future studies should examine how virtual nurses can be embedded in health system reforms and digital health policies to advance sustainable and person-centered chronic care delivery. 4.5 Implications for Nursing Practice, Research, and Policy Anthropomorphic virtual nurses can supplement traditional education by providing continuous, scalable, and personalized support in chronic disease management. These systems have the potential to alleviate workforce shortages by automating routine educational tasks, extending patient education beyond clinical settings into home and community environments, and supporting self-management between healthcare visits. To ensure their safe and effective integration into clinical practice, it is essential that human nurses actively engage in the co-design, validation, and implementation processes of such technologies. Moreover, targeted nurse training programs are necessary to equip the workforce with the skills required to effectively incorporate AI-driven tools into routine care. Despite the promising evidence base, the identification of only six RCTs signals a pressing need for more rigorous studies, involving larger and more diverse patient populations. Future research should prioritize assessing long-term behavioral and clinical outcomes, cost-effectiveness, and comparative evaluations between virtual nurses and other digital health modalities, such as chatbots and telehealth platforms. Additionally, research should focus on developing adaptive intervention designs that consider patients’ digital literacy, cultural backgrounds, and personal preferences to ensure that the interventions are both engaging and equitable. Most existing trials were conducted in high-income countries, underscoring the urgent need for studies in low- and middle-income countries where scalable solutions are most needed. Policymakers should thus consider integrating virtual nurse technologies into chronic care frameworks and broader digital health strategies to enhance reach and reduce disparities in health education and support. Addressing the digital divide through infrastructure investment, workforce training, and inclusive design is critical to prevent exacerbation of existing health inequities Nurses, as trusted providers, should play a central role in shaping policies and advocating AI-driven health innovations that prioritize safety, evidence-based, and patient-centered care. 4.6 Study Limitations This study had several limitations. First, the small number of included studies (n = 6) restricts the generalizability of the findings. Second, substantial clinical heterogeneity in terms of study populations, intervention modalities, and outcome measures precluded meta-analysis and complicated direct comparisons across studies. Third, methodological limitations exist, as several studies lacked blinding or adequate allocation concealment, potentially introducing bias. Finally, publication bias cannot be excluded, given that studies with null or negative findings may be underrepresented. In addition, most studies were conducted in high-income countries, primarily the USA and Australia, raising concerns about geographical and cultural transferability. Further research in low- and middle-income countries is needed to explore how contextual factors influence the implementation and outcomes of virtual nurse interventions. 5. Conclusion This systematic review synthesized current evidence on the effectiveness of anthropomorphic virtual nurse interventions in chronic disease self-management education. The findings indicate that virtual nurse interventions can effectively enhance disease-related health knowledge, support psychological well-being, and achieve high levels of user satisfaction among individuals with chronic diseases. These benefits are particularly salient when interventions are delivered in interactive, personalized, and flexible formats, making them especially relevant in settings where access to in-person nursing care is limited. However, improvements in clinical outcomes such as glycemic control and behavioral adherence are generally modest and often lacked statistical significance. This suggests that knowledge acquisition alone may not be sufficient to produce sustained physiological benefits without long-term engagement and integration of broader motivational, social, and structural support. Future research should prioritize long-term evaluation of behavioral and clinical outcomes, cost-effectiveness analyses, and tailoring of intervention designs to accommodate users’ digital literacy, cultural backgrounds, and individual preferences. It is crucial that these interventions align with evidence-based clinical guidelines and involve nurses and other healthcare professionals actively in their development and implementation. Overall, with thoughtful design, rigorous ethical oversight, and focused efforts on digital inclusion, virtual nursing technologies hold considerable promise as complementary tools to traditional care models. They have the potential to expand access to chronic disease self-management education and to support more equitable, person-centered, and sustainable healthcare delivery. Abbreviations RCT Randomized Controlled Trial HbA1c Glycated Hemoglobin (Hemoglobin A1c) PICO Population, Intervention, Comparison, Outcome SWiM Synthesis Without Meta–analysis PRISMA Preferred Reporting Items for Systematic Reviews and Meta–Analyses PROSPERO International Prospective Register of Systematic Reviews JBI Joanna Briggs Institute AFEQT Atrial Fibrillation Effect on Quality–of–Life Questionnaire MMAS 8–8–item Morisky Medication Adherence Scale PAM Patient Activation Measure HADS Hospital Anxiety and Depression Scale AI Artificial Intelligence mHealth Mobile Health Declarations Ethics approval and consent to participate: This study is a systematic review of published randomized controlled trials and did not involve direct human participants. Therefore, ethical approval and participant consent were not required. Human Ethics and Consent to Participate declarations not applicable. Competing interests: The authors declare no conflicts of interest. Clinical trial number Not applicable. Trial registration PROSPERO CRD420251038439. Funding: This work was supported by the National Science and Technology Council, Taiwan (NSTC 113-2314-B-A49-037) for English editing assistance. No additional funding was received. Author Contribution Tsuei-Wun Chang: Conceptualization, Methodology, Literature search, Data extraction, Writing – original draft, Critical revision of the manuscript.Hsin-Yi Chen: Literature search, Data extraction, Formal analysis.Yu-Chi Chen: Conceptualization, Supervision, Writing – review and editing, Project administration, Critical revision of the manuscript.All authors have made substantial contributions to the work, revised it critically, approved the final version, and agreed to be accountable for its accuracy and integrity. Acknowledgements: The authors would like to thank the library and information staff at National Yang Ming Chiao Tung University and National Taiwan University Hospital for their support in accessing full-text articles. Corresponding author : Yu-Chi Chen, Email: [email protected] Data Availability All data generated or analyzed during this study are included in this published article and its supplementary information files. References Acharya PC, Alba R, Krisanapan P, Acharya CM, Suppadungsuk S, Csongradi E, Cheungpasitporn W. AI-driven patient education in chronic kidney disease: evaluating Chatbot responses against clinical guidelines. Diseases. 2024;12(8):185. https://doi.org/10.3390/diseases12080185 . Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial intelligence–based chatbots for promoting health behavioral changes: Systematic review. Journal Med Internet research. 2023;25:e40789. https://doi.org/10.2196/40789 . 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Kidney Int Rep. 2020;5(7):1026–39. https://doi.org/10.1016/j.ekir.2020.04.021 . da Galvão G, Kavanagh DJ, Belpaeme T, Taylor L, Beeson K, Andrade J. Experiences of a motivational interview delivered by a robot: qualitative study. J Med Internet Res. 2018;20(5):e116. https://doi.org/10.2196/jmir.7737 . Giguère A, Zomahoun HTV, Carmichael PH, Uwizeye CB, Légaré F, Grimshaw JM, Massougbodji J. Printed educational materials: effects on professional practice and healthcare outcomes. Cochrane Database Syst Reviews. 2020;8. https://doi.org/10.1002/14651858.CD004398.pub4 . Gong E, Baptista S, Russell A, Scuffham P, Riddell M, Speight J, Oldenburg B. My diabetes coach, a mobile app–based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Med Internet Res. 2020;22(11):e20322. https://doi.org/10.2196/20322 . Graham-Brown MP, Smith AC, Greenwood SA. Digital health interventions in chronic kidney disease: levelling the playing field? Clin Kidney J. 2023;16(5):763–7. https://doi.org/10.1093/ckj/sfac259 . Guhl E, Althouse AD, Pusateri AM, Kimani E, Paasche-Orlow MK, Bickmore TW, Magnani JW. The atrial fibrillation health literacy information technology trial: pilot trial of a mobile health app for atrial fibrillation. JMIR cardio. 2020;4(1):e17162. https://doi.org/10.2196/17162 . Hacker K. (2024). The burden of chronic disease. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 8 (1), 112–119. https://doi.org/10.1016/j.mayocpiqo.2023.08.005 Jiang Z, Huang X, Wang Z, Liu Y, Huang L, Luo X. Embodied conversational agents for chronic diseases: scoping review. J Med Internet Res. 2024;26:e47134. https://doi.org/10.1136/bmjopen-2024-095360 . Magnani JW, Schlusser CL, Kimani E, Rollman BL, Paasche-Orlow MK, Bickmore TW. The atrial fibrillation health literacy information technology system: pilot assessment. JMIR cardio. 2017;1(2):1–12. https://doi.org/10.2196/cardio.8543 . Maguire R, McCann L, Kotronoulas G, Kearney N, Ream E, Armes J, Donnan PT. (2021). Real time remote symptom monitoring during chemotherapy for cancer: European multicentre randomised controlled trial (eSMART). bmj , 374 . https://doi.org/10.1136/bmj. n1647 Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Meinert E. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res. 2020;22–10–:e20346. https://doi.org/10.2196/2034 . Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Mu PF. (2020). Chapter 7: Systematic reviews of etiology and risk. In E. Aromataris & Z. Munn, editors, JBI Manual for Evidence Synthesis. Joanna Briggs Institute . https://doi.org/10.46658/JBIMES-20-07 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71 . Rosal MC, Heyden R, Mejilla R, Capelson R, Chalmers KA, DePaoli MR, Wiecha JM. A virtual world versus face-to-face intervention format to promote diabetes self-management among African American women: a pilot randomized clinical trial. JMIR Res protocols. 2014;3(4):e3412. https://doi.org/10.2196/resprot.3412 . Tao X, Zhu W, Chu M, Zhang Y. Nurse-led virtual interventions in managing chronic diseases: a protocol for a systematic review of randomised controlled trials. BMJ open. 2023;13(5):e070583. https://doi.org/10.1136/bmjopen-2022-070583 . Tongpeth J, Du H, Barry T, Clark RA. Effectiveness of an Avatar application for teaching heart attack recognition and response: a pragmatic randomized control trial. J Adv Nurs. 2020;76(1):297–311. https://doi.org/10.1111/jan.14210 . Wonggom P, Nolan P, Clark RA, Barry T, Burdeniuk C, Nesbitt K, Du H. Effectiveness of an avatar educational application for improving heart failure patients’ knowledge and self-care behaviors: A pragmatic randomized controlled trial. J Adv Nurs. 2020;76(9):2401–15. https://doi.org/10.1111/jan.14414 . Zhang L, Gallagher R, Du H, Barry T, Foote J, Ellis T, Clark RA. Evaluate the effect of virtual nurse-guided discharge education app on disease knowledge and symptom response in patients following coronary events. Int J Med Informatics. 2025;196:105818. https://doi.org/10.1016/j.ijmedinf.2025.105818 . Zhang X, Lewis S, Chen X, Zhou J, Wang X, Bucci S. Acceptability and Experience of A Smartphone Symptom Monitoring App for People With Psychosis in China (YouXin): A Qualitative Study. BMC Psychiatry. 2024;24(1):268. https://doi.org/10.1186/s12888-024-05687-2 . Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":649210,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA \u0026nbsp;\u0026nbsp;2020 flow diagram\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8182311/v1/beb990a469961209fd795d4c.jpeg"},{"id":104252349,"identity":"07922f20-d692-4567-a302-41831eaa97fe","added_by":"auto","created_at":"2026-03-09 16:18:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2201655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8182311/v1/0e764b24-2f24-4484-9f76-b4ae5ae4cca8.pdf"},{"id":98435420,"identity":"8d3588f9-a5b8-4ec4-a5d8-d23f0639057c","added_by":"auto","created_at":"2025-12-17 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Introduction","content":"\u003cp\u003eChronic diseases remain the leading cause of mortality and disability worldwide, imposing substantial burdens on patients, families, and healthcare systems (Bennett et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hacker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The aging global population has increased the prevalence of conditions such as diabetes, hypertension, and chronic kidney disease, all of which require continuous self-management and long-term support (Hacker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This rising health burden highlights the need for scalable, effective, and sustainable care models applicable across diverse healthcare systems (Tao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bin Sawad et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatient education is fundamental to chronic disease management, empowering individuals with the knowledge and skills necessary for self-care, adherence to treatment regimens, and early detection of potential complications, thereby improving health outcomes and quality of life (Correia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nurses play a central role in delivering this education; however, traditional methods\u0026mdash;such as face-to-face sessions and printed materials\u0026mdash;are often constrained by limited time, workforce shortages, and lack of individualized tailoring (Acharya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gigu\u0026egrave;re et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These challenges are particularly pronounced for patients requiring continuous, personalized support in home-based care environments.\u003c/p\u003e \u003cp\u003eDigital health innovations, particularly AI-driven technologies, have emerged as promising adjuncts to traditional nursing care (Al Kuwaiti et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wonggom et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Virtual nurses\u0026mdash;AI-based agents simulating nurse\u0026ndash;patient interactions\u0026mdash;provide personalized education, medication reminders, symptom monitoring, and emotional support through mobile apps, web platforms, and virtual reality (Milne-Ives et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unlike generic chatbots, virtual nurses incorporate anthropomorphic features such as facial expressions, vocal intonation, emotional recognition, and adaptive feedback, fostering more engaging and empathetic interactions (Aggarwal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Evidence suggests these interventions can enhance patient motivation, health literacy, and behavior change, particularly in home-based or remote care settings (Galv\u0026atilde;o et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chattopadhyay et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bin Sawad et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChallenges remain, including barriers for patients with limited digital literacy, especially older adults or socioeconomically disadvantaged groups (Arcury et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and ethical considerations such as data privacy, algorithmic bias, informed consent, and transparency (Al Kuwaiti et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Graham-Brown et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These issues underscore the importance of inclusive design and rigorous evaluation to ensure safety, efficacy, and equity in clinical practice.\u003c/p\u003e \u003cp\u003eAlthough previous systematic reviews have examined chatbots and AI applications in healthcare, few have specifically evaluated the clinical effectiveness of anthropomorphic virtual nurses in chronic disease self-management. Moreover, the impact of design factors\u0026mdash;such as interaction frequency, delivery modality, and personalization\u0026mdash;remains underexplored. Therefore, this systematic review aims to evaluate the effectiveness of virtual nurse interventions in supporting chronic disease self-management, focusing on health knowledge, behavioral outcomes, psychological well-being, and patient satisfaction, while examining design characteristics, user experience, and implementation considerations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Protocol\u003c/h2\u003e \u003cp\u003eThis systematic review adhered to the Synthesis Without Meta-analysis (SWiM) (Campbell et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) due to the heterogeneity of intervention designs and outcome measures (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Design\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe review was structured around the following PICO framework: Among adults with chronic diseases (Population), do virtual nurse interventions (Intervention), compared to usual care or alternative educational interventions (Comparison), improve outcomes (Outcome), specifically disease-related knowledge, self-management behaviors, psychological well-being, physiological indicators, patient satisfaction, and acceptability?\u003c/p\u003e \u003cp\u003eThe study population included adults aged 18 years or older diagnosed with chronic conditions such as diabetes, heart failure, or atrial fibrillation. The interventions encompassed virtual nursing systems featuring avatars, relational agents, or AI-based digital characters exhibiting human-like attributes (e.g., facial expressions, voice modulation, and emotional responsiveness). The comparison group consisted of usual care or other educational interventions delivered by healthcare professionals or digital platforms. The primary outcome focused on improvement in disease-related knowledge and awareness, while secondary outcomes included self-management behaviors, physiological indicators, psychological well-being, patient satisfaction, and acceptability.\u003c/p\u003e \u003cp\u003eEligible studies for inclusion were randomized controlled trials assessing virtual nursing systems designed to facilitate patient education, coaching, or chronic disease self-management. The synthesis compared intervention delivery formats, outcome domains, and patient-reported measures to ascertain overall effectiveness.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Search Method\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA comprehensive literature search was conducted using five electronic databases: the Cochrane Library, PubMed, CINAHL, MEDLINE, and EMBASE. The search covered studies published between January 2014 and April 2025 and was limited to peer-reviewed articles published in English and Chinese.\u003c/p\u003e \u003cp\u003eKeywords and Medical Subject Headings (MeSH) were derived from the PICO framework and included: \u003cem\u003e\u0026ldquo;virtual nurse,\u0026rdquo; \u0026ldquo;avatar,\u0026rdquo; \u0026ldquo;relational agent,\u0026rdquo; \u0026ldquo;embodied conversational agent,\u0026rdquo; \u0026ldquo;digital health,\u0026rdquo; \u0026ldquo;self-management,\u0026rdquo; \u0026ldquo;chronic disease,\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;patient education.\u0026rdquo;\u003c/em\u003e Boolean operators (\u0026ldquo;AND\u0026rdquo; and \u0026ldquo;OR\u0026rdquo;) were used to optimize both the sensitivity and specificity of the search strategy. The detailed search strategies for each database are provided in Table S2.\u003c/p\u003e \u003cp\u003eAll studies identified for inclusion were cross-referenced with those included in a larger systematic review by the authors (unpublished) of all risk assessment scales. Additionally, the reference lists of systematic reviews found in the search were screened to identify potential articles for inclusion.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Inclusion and/or Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe inclusion criteria were: (1) RCTs; (2) adult participants (\u0026ge;\u0026thinsp;18 years) diagnosed with chronic diseases; (3) interventions involving virtual nurse or avatar-based systems; and (4) reporting of relevant outcomes related to patient education or self-management.\u003c/p\u003e \u003cp\u003eReviews, protocols, conference abstracts, clinical guidelines, and studies without outcome data were excluded. The reference lists of all the included studies were manually screened for additional relevant articles. When full-text articles were not accessible, the corresponding authors were contacted.\u003c/p\u003e \u003cp\u003eEndNote reference software was used for article screening and data extraction. Duplicates were removed and two reviewers independently assessed the study titles and abstracts to identify potentially relevant studies that met the inclusion criteria. Those who did not meet the inclusion criteria were excluded and their reasons were recorded. Following the title and abstract screening, full-text screening was performed independently by two reviewers to assess eligibility. Any disagreements were discussed and resolved by a third reviewer, as necessary.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Search Outcome\u003c/h2\u003e \u003cp\u003eA total of 54 unique citations were initially identified through database searches. Following screening, eight articles were selected for full-text eligibility assessment. Upon detailed review, two articles were excluded: one because it was not a RCT, and the other for lacking relevant outcome data. Thus, six studies were included in the final analysis. The study selection process is illustrated in the PRISMA flowchart shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quality Appraisal\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe methodological quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Randomized Controlled Trials (2020). The checklist consists of 13 items, each scored as \u0026ldquo;Yes\u0026rdquo; (1 point), \u0026ldquo;No,\u0026rdquo; \u0026ldquo;Unclear,\u0026rdquo; or \u0026ldquo;Not applicable\u0026rdquo; (0 points), with a maximum score of 13. Studies with scores\u0026thinsp;\u0026gt;\u0026thinsp;7 were considered methodologically adequate (Moola et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo independent reviewers performed the evaluations. Any disagreements were resolved through discussion and consultation with a third reviewer when consensus could not be reached.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data Extraction and Synthesis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData were independently extracted and summarized using a standardized template. The extracted information included author(s), publication year, country, target population, disease type, sample size, description of the virtual nurse intervention, delivery platform, and measured outcomes. Two reviewers independently conducted the data extraction. Disagreements regarding the extracted data were resolved through discussion between the reviewers, and, where needed, a third reviewer was consulted to resolve discrepancies.\u003c/p\u003e \u003cp\u003eDue to the heterogeneity in intervention designs, durations, and assessment tools, a meta-analysis was not appropriate. Synthesis without meta-analysis was conducted to categorize the interventions and summarize the outcomes across five domains: health knowledge, behavioral changes, physiological indicators, psychological well-being, and patient satisfaction.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of Included Studies\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA summary of these studies is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which details disease focus, delivery platforms, and outcome domains. All six included studies were RCTs conducted between 2014 and 2022, primarily in the United States or Australia. A total of 533 participants were enrolled across the studies, all of whom were adults (\u0026ge;\u0026thinsp;18 years) diagnosed with chronic conditions that included type 2 diabetes mellitus, heart failure, atrial fibrillation, or acute coronary syndrome.\u003c/p\u003e \u003c/div\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\u003eSummary of Included Studies on Virtual Nurse Interventions (N\u0026thinsp;=\u0026thinsp;6)\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=\"char\" char=\".\" 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\u003eFirst\u003c/p\u003e \u003cp\u003eauthor (Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisease Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType and Interface of Virtual Nurse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEducational Content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hispanic Black women\u003c/p\u003e \u003cp\u003eSex: all female\u003c/p\u003e \u003cp\u003eMean age: 52 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType 2 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVirtual world (Second Life), group-based interactive avatars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiet, exercise, self-monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 weeks (90 min/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHbA1c\u003c/b\u003e decreased by 3.2% in the virtual group and 4.9% in the in-person group (p\u0026thinsp;=\u0026thinsp;.52).\u003c/p\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e increased by 18.4% in the virtual group (p\u0026thinsp;=\u0026thinsp;.52).\u003c/p\u003e \u003cp\u003e\u003cb\u003eDepression\u003c/b\u003e improved more in the in-person group (p\u0026thinsp;=\u0026thinsp;.051).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSatisfaction\u003c/b\u003e: 97% preferred face-to-face vs. 80% preferred virtual (p\u0026thinsp;=\u0026thinsp;.490); 100% vs. 97% would recommend (p\u0026thinsp;=\u0026thinsp;1.0).\u003c/p\u003e \u003cp\u003e\u003cb\u003eMedication adherence\u003c/b\u003e decreased by 8.6% in the face-to-face group and increased by 1.2% in the virtual group; no significant between-group difference (p\u0026thinsp;=\u0026thinsp;.298).\u003c/p\u003e \u003cp\u003e\u003cb\u003eBlood Pressure\u003c/b\u003e: Systolic BP remained unchanged in the face-to-face group and increased by 1.5% in the virtual group; diastolic BP decreased by 2.0% and increased by 0.9%, respectively; no significant between-group differences (systolic BP p\u0026thinsp;=\u0026thinsp;.609; diastolic BP p\u0026thinsp;=\u0026thinsp;.675).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSelf-management\u003c/b\u003e: improved in both groups (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with no between-group difference (p\u0026thinsp;=\u0026thinsp;.268).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with atrial fibrillation\u003c/p\u003e \u003cp\u003eSex: 19 males, 12 females\u003c/p\u003e \u003cp\u003eMean age: 68 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtrial Fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecomputer-animated relational agent (Tanya)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAF education, symptoms, medication adherence, self-management strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 days (average 17.8 days of use)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAFEQT scores\u003c/b\u003e: improved from 64.5 (baseline) to 76.3 at 30 days (p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e \u003cp\u003e\u003cb\u003eMMAS-8\u003c/b\u003e: increased from 7.3 (baseline) to 7.7 at 30 days (p\u0026thinsp;=\u0026thinsp;.01).\u003c/p\u003e \u003cp\u003e\u003cb\u003ePAM\u003c/b\u003e: increased from 3.0 (baseline) to 3.4 at 30 days (p\u0026thinsp;=\u0026thinsp;.33).\u003c/p\u003e \u003cp\u003e\u003cb\u003eAcceptability\u003c/b\u003e: most found agent useful and trustworthy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongpeth et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute Coronary Syndrome patients\u003c/p\u003e \u003cp\u003eSex: 44 males, 26 females\u003c/p\u003e \u003cp\u003eMean age: 64.7 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcute Coronary Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTablet-based avatar app (Nurse Cora) with animation and voice interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS symptoms, action plan, knowledge/attitude education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 months (mean review average 6 per person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eACS knowledge\u003c/b\u003e increased from 58.35% to 83.55% (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSymptom knowledge, attitudes, and beliefs\u003c/b\u003e improved in the intervention group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, .009, .001).\u003c/p\u003e \u003cp\u003e\u003cb\u003eACS Response Index\u003c/b\u003e scores were higher at 1 and 6 months (p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e \u003cp\u003e\u003cb\u003eAmbulance use\u003c/b\u003e was greater in the intervention group (33.3% vs. 18.2%, p\u0026thinsp;=\u0026thinsp;.008).\u003c/p\u003e \u003cp\u003e\u003cb\u003eConfidence\u003c/b\u003e: 85% reported feeling more confident recognizing and responding to symptoms.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSatisfaction\u003c/b\u003e: all 33 participants were satisfied with the Avatar app.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdults with T2DM\u003c/p\u003e \u003cp\u003eSex: 109 males, 78 females\u003c/p\u003e \u003cp\u003eMean age: 56.9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType 2 Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMobile app (MDC) with virtual agent \"Laura\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBlood glucose management, lifestyle coaching, medication education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 months (average 243 min/person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eEngagement\u003c/b\u003e: participants completed 1942 chats.\u003c/p\u003e \u003cp\u003e\u003cb\u003eHbA1c\u003c/b\u003e: decreased by 0.33% in the intervention group and 0.20% in the control group; no significant between-group difference (\u0026minus;\u0026thinsp;0.04%, 95% CI \u0026minus;\u0026thinsp;0.45 to 0.36, p\u0026thinsp;=\u0026thinsp;.83).\u003c/p\u003e \u003cp\u003e\u003cb\u003eHRQoL\u003c/b\u003e: utility scores improved more in the intervention arm (between-arm difference 0.04, 95% CI 0.00 to 0.07, p\u0026thinsp;=\u0026thinsp;.04).\u003c/p\u003e \u003cp\u003e\u003cb\u003eHADS anxiety score\u003c/b\u003e: significant between-arm difference in mean change at 6 months (\u0026minus;\u0026thinsp;0.89, 95% CI \u0026minus;\u0026thinsp;1.74 to \u0026minus;\u0026thinsp;0.04, p\u0026thinsp;=\u0026thinsp;.04).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuhl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with atrial fibrillation\u003c/p\u003e \u003cp\u003eSex: 58 males, 62 females\u003c/p\u003e \u003cp\u003eMean age: 72.1 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtrial Fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmartphone-based virtual nurse \"Tanya\" + HR monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEmotional support, quality of life enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 days (average 40.7 min )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eAFEQT scores\u003c/b\u003e: higher in the intervention group (total 4.5, 95% CI 0.6 to 8.3, p\u0026thinsp;=\u0026thinsp;.03).\u003c/p\u003e \u003cp\u003e\u003cb\u003eAnticoagulant adherence\u003c/b\u003e: better self-reported adherence at 30 days (3.5% vs. 23.2%, adjusted difference 16.6%, 95% CI 2.8% to 30.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e\u003cb\u003eAcceptability\u003c/b\u003e: most found the agent useful, informative, and trustworthy; median satisfaction 6/7, easy to use (median 1), content somewhat repetitive (median 5).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with heart failure\u003c/p\u003e \u003cp\u003eSex: 29 males, 7 females\u003c/p\u003e \u003cp\u003eMean age: 67.5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTablet-based avatar app\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePersonalized health advice and lifestyle support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90 days\u003c/p\u003e \u003cp\u003e(2.6\u0026ndash;4 times/month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eKnowledge\u003c/b\u003e: intervention group had greater improvement in HF knowledge at 90 days (22.2% vs. 3.7%, p\u0026thinsp;=\u0026thinsp;.002), with a smaller non-significant difference at 30 days (15.3% vs. 3.5%, p\u0026thinsp;=\u0026thinsp;.061).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSelf-care\u003c/b\u003e: improved only in the intervention group at 30 days (11.9% vs. \u0026minus;4.3%) and 90 days (17.3% vs. \u0026minus;2.9%, p\u0026thinsp;=\u0026thinsp;.244).\u003c/p\u003e \u003cp\u003e\u003cb\u003eHealthcare use\u003c/b\u003e: no significant differences in hospital readmissions at 30 or 90 days (p\u0026thinsp;\u0026gt;\u0026thinsp;.3).\u003c/p\u003e \u003cp\u003e\u003cb\u003eSatisfaction\u003c/b\u003e: overall satisfaction with the avatar app was high (91.3%), with most finding it easy to understand (100%), concise and easy to follow (95%).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eT2DM\u0026thinsp;=\u0026thinsp;Type 2 Diabetes Mellitus; ACS\u0026thinsp;=\u0026thinsp;Acute Coronary Syndrome; AF\u0026thinsp;=\u0026thinsp;Atrial Fibrillation; HF\u0026thinsp;=\u0026thinsp;Heart Failure; NST\u0026thinsp;=\u0026thinsp;Not statistically tested; AFEQT\u0026thinsp;=\u0026thinsp;Atrial Fibrillation Effect on Quality-of-Life questionnaire; MMAS-8\u0026thinsp;=\u0026thinsp;8-Item Morisky Medication Adherence Scale; PAM\u0026thinsp;=\u0026thinsp;Patient Activation Measure; HRQoL\u0026thinsp;=\u0026thinsp;Health-Related Quality of Life; HADS\u0026thinsp;=\u0026thinsp;Hospital Anxiety and Depression Scale; BP\u0026thinsp;=\u0026thinsp;Blood Pressure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVirtual nurse interventions vary in design and platform, encompassing embodied avatars, relational agents, and AI-driven digital characteristics. These interventions were delivered in a range of formats, including tablet-based applications (e.g., Nurse Cora and Tanya), mobile health applications (e.g., My Diabetes Coach), virtual reality platforms (e.g., Second Life), and web-based systems. Interventions targeted key domains, such as disease-specific knowledge, self-management support, emotional well-being, and health behavior change, with durations ranging from 30 days to 12 months.\u003c/p\u003e \u003cp\u003eThe outcomes assessed included HbA1c levels, medication adherence, quality of life, health knowledge, psychological indicators (e.g., depression and anxiety), and user satisfaction. Most studies reported positive effects in at least one domain, particularly on improvements in health knowledge, quality of life, and patient satisfaction. A summary of the included studies, including sample characteristics, virtual nurse formats, and key findings, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A comparison of the outcome domains is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eComparison of Outcomes Across Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003cp\u003e(Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKnowledge Improvement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eBehavioural change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePhysiological\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePsychological\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUser\u003c/p\u003e \u003cp\u003eSatisfaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBehavioral Response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedication Adherence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelf-management\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGlycemic\u003c/p\u003e \u003cp\u003e(HbA1C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBlood Pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDepression Symptoms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongpeth et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuhl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e = Positive change (not statistically significant) ; \u003cb\u003e+\u003c/b\u003e = Statistically significant improvement ;\u003cb\u003e\u0026ndash;=\u003c/b\u003e Not reported.「\u003cem\u003eFor details, see Section 3.5\u003c/em\u003e」\u003c/p\u003e \u003cp\u003eAbbreviations: T2DM\u0026thinsp;=\u0026thinsp;Type 2 Diabetes Mellitus; ACS\u0026thinsp;=\u0026thinsp;Acute Coronary Syndrome; AF\u0026thinsp;=\u0026thinsp;Atrial Fibrillation; HF\u0026thinsp;=\u0026thinsp;Heart Failure; NST\u0026thinsp;=\u0026thinsp;Not statistically tested\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\u003eDue to the substantial heterogeneity in intervention modalities, outcome measures, follow-up durations, and evaluation tools, a meta-analysis was not feasible. Therefore, the findings were synthesized using a narrative approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Quality of Studies\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe methodological quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Randomized Controlled Trials (2020). As shown in Table S3, all six studies received scores ranging from 9 to 10 out of a maximum of 13, indicating moderate-to-high methodological quality.\u003c/p\u003e \u003cp\u003eAll studies clearly described their randomization procedures and participant characteristics, and most demonstrated baseline comparability, consistent intervention delivery, the use of reliable outcome measures, and appropriate statistical analyses. However, several methodological limitations were noted. Specifically, allocation concealment and the blinding of participants and outcome assessors were either unreported or not feasible, largely because of the interactive and visually distinguishable nature of virtual nurse interventions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Technology Formats and Functions of Virtual Nurses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll included studies assessed the participants\u0026rsquo; digital literacy or device access prior to enrollment to ensure the feasibility of using virtual nurse technologies. Overall, the interventions were well received, and they demonstrated positive effects on health knowledge, self-management behaviors, and selected physiological and psychological outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The technological formats and functional characteristics of virtual nursing systems vary along two primary dimensions:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. 1. Interaction Modes and Interface Types\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual nurse interventions are delivered via mobile apps, tablets, websites, or virtual reality environments. Most systems employ human-like avatars that interact through text-, voice-, or video-based communication to simulate natural nurse\u0026ndash;patient conversations. For example, Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) implemented a virtual-world group education model using Second Life; Gong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) utilized a voice-based agent named Laura to deliver diabetes education; Magnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) employed a computer-animated relational agent named Tanya to provide atrial fibrillation education; and Wonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) developed a tablet-based avatar application to educate patients with heart failure (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Educational Content, Duration, and Delivery Flexibility\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe educational content was aligned with evidence-based clinical guidelines and focused on core self-management areas, including symptom monitoring, medication adherence, dietary and lifestyle modifications, and emotional well-being. Interventions incorporated multimedia elements, such as videos, infographics, and interactive quizzes. The study duration ranged from 30 days to 12 months, with interaction frequencies varying from daily to weekly. Several systems have enabled self-paced learning and provided adaptive feedback based on user input, thereby enhancing personalization and flexibility.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Outcome Indicators\u003c/h2\u003e \u003cp\u003eOutcomes were synthesized narratively across five key domains: health knowledge, self-management behaviors, physiological indicators, psychological wellbeing, and user satisfaction. A cross-study comparison of the outcomes is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Health Knowledge and Disease Awareness\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSignificant improvements in the disease-specific knowledge were observed in the included studies. Tongpeth et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported marked increases in symptom recognition among patients with acute coronary syndrome, rising from 58.35% to 83.55%, along with gains in symptom knowledge (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), attitudes (p\u0026thinsp;=\u0026thinsp;.009), and beliefs (p\u0026thinsp;=\u0026thinsp;.001). Acute Coronary Syndrome Response Index scores remained elevated at both 1 and 6 months (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), with 85% of the participants expressing greater confidence in recognizing and responding to symptoms. Wonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated improved heart failure knowledge, with a 22.2% increase in the intervention group compared to 3.7% in the control group (p\u0026thinsp;=\u0026thinsp;.002). Gong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) did not report direct knowledge scores but showed high user engagement, recording 1,942 chatbot sessions, suggesting effective educational delivery. (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for an outcome summary of the studies).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Self-management and Behavioral Change\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePositive behavioral outcomes have been reported. Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found an 18.4% increase in physical activity in a virtual group compared to a 22.5% decrease in a face-to-face group (p\u0026thinsp;=\u0026thinsp;.10). Both groups showed improvement in diabetes self-efficacy (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with no significant difference between them (p\u0026thinsp;=\u0026thinsp;.268).\u003c/p\u003e \u003cp\u003eMedication adherence outcomes were similarly favorable. Guhl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified higher self-reported anticoagulant adherence at 30 days in the intervention group vs. control, with an adjusted difference of 16.6% (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Magnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that MMAS-8 adherence scores increased from 7.3 to 7.7 over 30 days (p\u0026thinsp;=\u0026thinsp;.01), although patient activation measured by PAM remained unchanged (p\u0026thinsp;=\u0026thinsp;.33). Wonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported improved self-care in the intervention group at both 30 and 90 days with no significant differences in hospital readmissions. (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for a comparative summary of behavioral changes).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Physiological Outcomes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eModest reductions in HbA1c levels were observed in patients with diabetes. Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported a 3.2% reduction in the virtual group (p\u0026thinsp;=\u0026thinsp;.186) and a significant 4.9% decrease in the face-to-face group (p\u0026thinsp;=\u0026thinsp;.019) with no significant differences between the groups (p\u0026thinsp;=\u0026thinsp;.52). Gong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted a modest reduction of 0.33% in the intervention group compared with 0.20% in the control group (p\u0026thinsp;=\u0026thinsp;.83).\u003c/p\u003e \u003cp\u003eRegarding blood pressure, Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found no significant changes over time. In the face-to-face group, systolic BP remained at 126.0 mmHg (p\u0026thinsp;=\u0026thinsp;.808) with a slight decrease in diastolic BP (p\u0026thinsp;=\u0026thinsp;.733). The virtual group showed slight increases in systolic and diastolic BP (p\u0026thinsp;=\u0026thinsp;.233 and p\u0026thinsp;=\u0026thinsp;.600, respectively). Between-group differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.60). (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the glycemic and blood pressure outcomes).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4 Psychological Wellbeing and Emotional Support\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual nursing systems have been shown to provide psychological benefits. Gong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported significant reductions in anxiety and depression with greater improvements in health-related quality of life utility scores in the intervention group (p\u0026thinsp;=\u0026thinsp;.04). HADS anxiety scores similarly favored the intervention group at six months (p\u0026thinsp;=\u0026thinsp;.04). Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed reduced depressive symptoms in both groups, with marginally greater improvements in the face-to-face group (p\u0026thinsp;=\u0026thinsp;.051).\u003c/p\u003e \u003cp\u003eNotably, these psychological outcomes were accompanied by quality of life gains. Guhl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported higher AFEQT scores in patients receiving virtual interventions (p\u0026thinsp;=\u0026thinsp;.03), and Magnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that scores increased from 64.5 to 76.3 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01). These findings suggest that virtual nursing systems may provide psychological support, in addition to educational benefits (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for depression and quality of life outcomes).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.4.5 Patient Satisfaction and Acceptability\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUser satisfaction with the virtual nursing system was consistently high. Guhl et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that most participants found the virtual agent useful, informative, and trustworthy (median satisfaction 6/7), with the tool rated easy to use, though some noted that the content was repetitive. Wonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observed an overall satisfaction rate of 91.3%, with nearly all participants finding the app clear and easy to follow. Magnani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlighted the positive perceptions of daily monitoring, and Tongpeth et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that all participants were satisfied with an avatar app.\u003c/p\u003e \u003cp\u003eHowever, several studies have identified technical usability issues, such as interface complexity or limited interactivity, suggesting the need for improved interface design and user-centered functionality in future development. (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for detailed satisfaction outcomes.)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eThis systematic review assessed the effectiveness of anthropomorphic virtual nurse interventions in supporting chronic disease self-management, with particular focus on improvements in health knowledge, self-care behaviors, psychological well-being, and user satisfaction. Overall, the evidence suggests that virtual nursing interventions are feasible, acceptable, and beneficial in enhancing patient education and engagement. Notably, consistent improvements were observed in disease-specific knowledge and patient satisfaction, underscoring the potential of these systems as valuable educational tools. However, changes in clinical outcomes\u0026mdash;such as glycated hemoglobin (HbA1c) levels and blood pressure\u0026mdash;were generally modest and frequently lacked statistical significance, indicating that achieving meaningful physiological improvements may require prolonged or more intensive intervention.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Educational Functions and Delivery Models\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual nursing systems simulate nurse\u0026ndash;patient interactions through personalized education, behavioral coaching, and emotional support. In this review, most systems employed humanlike avatars capable of voice-, text-, or video-based interactions, enabling real-time or asynchronous communication. Wonggom et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Tongpeth et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated significant improvements in disease-specific knowledge and symptom recognition, highlighting the value of interactive and multimodal learning environments.\u003c/p\u003e \u003cp\u003eThese findings align with prior studies showing that repetitive, self-paced digital education enhances health literacy and retention (Chattopadhyay et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, virtual agents using narrative dialogues and simulated scenarios, as seen in relational agents, may foster emotional relevance and deeper understanding (Correia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gigu\u0026egrave;re et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, systems that integrate narrative dialogues or simulated scenarios, as seen in relational agents, may increase emotional relevance and deepen our understanding (Chattopadhyay et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Galv\u0026atilde;o et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar to eHealth interventions in oncology and nephrology settings, the format and frequency of virtual nurse interactions vary across studies, ranging from daily app-based monitoring (Gong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to weekly education sessions in virtual group classes (Rosal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As Flythe et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted, in tablet-based chronic kidney disease monitoring, the optimal usage frequency must balance engagement and burden to prevent dropouts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Design Challenges and Considerations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlthough virtual nursing systems have strong educational potential, their design and implementation present notable challenges. A key issue lies in the lack of standardization across interventions, particularly in terms of content scope, interaction frequency, and feedback mechanisms, which hampers the comparability and generalizability of the findings. Although some studies have employed high-fidelity relational agents and sophisticated interfaces, many systems have failed to incorporate adaptive features that tailor educational content based on patient needs, responses, or digital literacy levels. Designing an effective system requires a balance between technological complexity and user accessibility. As highlighted by Graham-Brown et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), involving target users early in the co-design process can help ensure that content remains clinically appropriate, interfaces are intuitive, and user burden is minimized. Such participatory approaches are critical for creating virtual nurse interventions that are both person-centered and sustainable in real-world settings. Another critical yet often-overlooked factor is digital health literacy, which significantly influences the ability of patients to engage with virtual nurse technologies. Most of the included studies did not assess or report on the digital competence of participants, limiting insights into how user readiness may have affected the outcomes. As noted by Arcury et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Estrela et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), older adults and socioeconomically disadvantaged populations are more likely to face challenges in using digital health tools because of limited access, skills, or confidence. Virtual nurse systems should incorporate multilingual support, onboarding assistance, and intuitive low-burden interfaces. Furthermore, offering structured digital health literacy training, either prior to or alongside intervention delivery, can help reduce disparities in uptake and improve the effectiveness of these tools among marginalized groups (Bui et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Addressing these user-level barriers is essential to ensuring that virtual nursing interventions do not inadvertently widen the digital divide.\u003c/p\u003e \u003cp\u003eFurther design considerations involve aligning educational content with clinical practice guidelines. As shown by Acharya et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), AI-generated health education materials, including those integrated into virtual nurse systems, may deviate from established clinical standards, such as evidence-based chronic kidney disease guidelines. This raises concerns regarding the accuracy, safety, and credibility of the information delivered to patients. To mitigate these risks, nurses and clinical experts must remain actively involved in the development, validation, and quality assurance of the content. Such oversight ensures that virtual nurse interventions not only meet educational goals, but also uphold clinical integrity and patient safety.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Clinical Integration and Population Considerations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual nursing technologies have the potential to support care in both home and outpatient settings, particularly in contexts where access to in-person nursing education is limited. The consistently high levels of user satisfaction observed across the studies in this review underscore their acceptability among community-dwelling adults and suggest the feasibility of a broader implementation. However, several population-specific challenges have hindered its widespread adoption. Older adults or individuals with limited digital health literacy may face barriers such as unfamiliarity with technology, inconsistent Internet access, or physical limitations affecting device use (Bui, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, nurses may be reluctant to integrate digital tools into practice because of insufficient training, workflow constraints, or a lack of institutional support (Anderson et al., 2021; Flythe et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo maximize their clinical impact, virtual nursing systems should be integrated into routine workflows, including discharge planning, chronic disease management programs, and telehealth services. Embedding these tools in existing care structures can enhance their continuity and relevance. Furthermore, collaborative implementation models\u0026mdash;involving nurses in system co-design, user testing, and outcome evaluation\u0026mdash;can improve usability, trust, and adoption (Galv\u0026atilde;o et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Graham-Brown et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, institutional investments in infrastructure, technical support, and continuing education for healthcare providers are essential. Equipping nurses with the competencies to guide patients through virtual care systems is critical for a successful and sustainable integration into chronic disease care (Anderson et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Effectiveness Variability and Future Research\u003c/h2\u003e \u003cp\u003eWhile this review identified consistent improvements in knowledge, psychological well-being, and user satisfaction, the effects on behavioral changes and physiological outcomes such as HbA1c and blood pressure were less conclusive. For example, Gong et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Rosal et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed that reductions in HbA1c fell below thresholds for clinical relevance, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e under \u0026ldquo;Physiological Outcomes,\u0026rdquo; among participants using virtual nurse interventions. This aligns with the broader eHealth literature, which consistently shows that knowledge acquisition alone rarely leads to sustained learning without motivational, social, and environmental reinforcement (Aggarwal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e Maguire et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Milne-Ives et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo advance this field, future research should prioritize long-term RCTs with extended follow-ups to evaluate the sustained impact of virtual nurse interventions on behavioral adherence and clinical outcomes. Comparative trials exploring different interaction modalities (e.g., video vs. text-based agents) and blended models (human\u0026thinsp;+\u0026thinsp;virtual) that combine human and virtual support could help identify optimal delivery strategies. Moreover, incorporating assessments of digital health literacy is essential for understanding how user characteristics influence intervention effectiveness, as well as for guiding the tailoring of design elements that can reduce disparities in use and outcomes. Additionally, future studies should examine how tailored design elements such as adaptive feedback, culturally appropriate content, and emotionally responsive interfaces can reduce inequities in access and outcomes across diverse patient populations. Exploring these customization factors is critical to enhancing both engagement and clinical utility.\u003c/p\u003e\u003cp\u003eFinally, the evaluation of cost-effectiveness and scalability is vital to determining the broader impact of virtual nurse programs. Although initial development and implementation may require investment, evidence suggests that well-designed systems can help reduce hospital readmissions, improve self-care adherence, and alleviate the burden on healthcare personnel (Milne-Ives et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bin Sawad et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future studies should examine how virtual nurses can be embedded in health system reforms and digital health policies to advance sustainable and person-centered chronic care delivery.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Implications for Nursing Practice, Research, and Policy\u003c/h2\u003e \u003cp\u003eAnthropomorphic virtual nurses can supplement traditional education by providing continuous, scalable, and personalized support in chronic disease management. These systems have the potential to alleviate workforce shortages by automating routine educational tasks, extending patient education beyond clinical settings into home and community environments, and supporting self-management between healthcare visits. To ensure their safe and effective integration into clinical practice, it is essential that human nurses actively engage in the co-design, validation, and implementation processes of such technologies. Moreover, targeted nurse training programs are necessary to equip the workforce with the skills required to effectively incorporate AI-driven tools into routine care.\u003c/p\u003e \u003cp\u003eDespite the promising evidence base, the identification of only six RCTs signals a pressing need for more rigorous studies, involving larger and more diverse patient populations. Future research should prioritize assessing long-term behavioral and clinical outcomes, cost-effectiveness, and comparative evaluations between virtual nurses and other digital health modalities, such as chatbots and telehealth platforms. Additionally, research should focus on developing adaptive intervention designs that consider patients\u0026rsquo; digital literacy, cultural backgrounds, and personal preferences to ensure that the interventions are both engaging and equitable. Most existing trials were conducted in high-income countries, underscoring the urgent need for studies in low- and middle-income countries where scalable solutions are most needed. Policymakers should thus consider integrating virtual nurse technologies into chronic care frameworks and broader digital health strategies to enhance reach and reduce disparities in health education and support. Addressing the digital divide through infrastructure investment, workforce training, and inclusive design is critical to prevent exacerbation of existing health inequities Nurses, as trusted providers, should play a central role in shaping policies and advocating AI-driven health innovations that prioritize safety, evidence-based, and patient-centered care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Study Limitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study had several limitations. First, the small number of included studies (n\u0026thinsp;=\u0026thinsp;6) restricts the generalizability of the findings. Second, substantial clinical heterogeneity in terms of study populations, intervention modalities, and outcome measures precluded meta-analysis and complicated direct comparisons across studies. Third, methodological limitations exist, as several studies lacked blinding or adequate allocation concealment, potentially introducing bias. Finally, publication bias cannot be excluded, given that studies with null or negative findings may be underrepresented. In addition, most studies were conducted in high-income countries, primarily the USA and Australia, raising concerns about geographical and cultural transferability. Further research in low- and middle-income countries is needed to explore how contextual factors influence the implementation and outcomes of virtual nurse interventions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis systematic review synthesized current evidence on the effectiveness of anthropomorphic virtual nurse interventions in chronic disease self-management education. The findings indicate that virtual nurse interventions can effectively enhance disease-related health knowledge, support psychological well-being, and achieve high levels of user satisfaction among individuals with chronic diseases. These benefits are particularly salient when interventions are delivered in interactive, personalized, and flexible formats, making them especially relevant in settings where access to in-person nursing care is limited. However, improvements in clinical outcomes such as glycemic control and behavioral adherence are generally modest and often lacked statistical significance. This suggests that knowledge acquisition alone may not be sufficient to produce sustained physiological benefits without long-term engagement and integration of broader motivational, social, and structural support.\u003c/p\u003e \u003cp\u003eFuture research should prioritize long-term evaluation of behavioral and clinical outcomes, cost-effectiveness analyses, and tailoring of intervention designs to accommodate users\u0026rsquo; digital literacy, cultural backgrounds, and individual preferences. It is crucial that these interventions align with evidence-based clinical guidelines and involve nurses and other healthcare professionals actively in their development and implementation. Overall, with thoughtful design, rigorous ethical oversight, and focused efforts on digital inclusion, virtual nursing technologies hold considerable promise as complementary tools to traditional care models. They have the potential to expand access to chronic disease self-management education and to support more equitable, person-centered, and sustainable healthcare delivery.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Controlled Trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated Hemoglobin (Hemoglobin A1c)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePICO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation, Intervention, Comparison, Outcome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWiM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSynthesis Without Meta\u0026ndash;analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta\u0026ndash;Analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePROSPERO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Prospective Register of Systematic Reviews\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJoanna Briggs Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFEQT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtrial Fibrillation Effect on Quality\u0026ndash;of\u0026ndash;Life Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e8\u0026ndash;8\u0026ndash;item Morisky Medication Adherence Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Activation Measure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHADS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospital Anxiety and Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emHealth\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMobile Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eThis study is a systematic review of published randomized controlled trials and did not involve direct human participants. Therefore, ethical approval and participant consent were not required.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTrial registration\u003c/strong\u003e \u003cp\u003ePROSPERO CRD420251038439.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Science and Technology Council, Taiwan (NSTC 113-2314-B-A49-037) for English editing assistance. No additional funding was received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTsuei-Wun Chang: Conceptualization, Methodology, Literature search, Data extraction, Writing \u0026ndash; original draft, Critical revision of the manuscript.Hsin-Yi Chen: Literature search, Data extraction, Formal analysis.Yu-Chi Chen: Conceptualization, Supervision, Writing \u0026ndash; review and editing, Project administration, Critical revision of the manuscript.All authors have made substantial contributions to the work, revised it critically, approved the final version, and agreed to be accountable for its accuracy and integrity.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the library and information staff at National Yang Ming Chiao Tung University and National Taiwan University Hospital for their support in accessing full-text articles.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorresponding author\u003c/b\u003e: Yu-Chi Chen, Email: [email protected]\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharya PC, Alba R, Krisanapan P, Acharya CM, Suppadungsuk S, Csongradi E, Cheungpasitporn W. 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BMC Psychiatry. 2024;24(1):268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12888-024-05687-2\u003c/span\u003e\u003cspan address=\"10.1186/s12888-024-05687-2\" targettype=\"DOI\" 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Virtual nurse. Chronic disease. Self-management. Patient education. Patient satisfaction","lastPublishedDoi":"10.21203/rs.3.rs-8182311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8182311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Chronic diseases require ongoing self-management, yet patients often struggle with knowledge, behavioral adherence, and emotional well-being. Virtual nurse interventions, digital systems simulating nurse–patient interactions, offer a potential strategy to support self-management. This review aimed to evaluate their effectiveness in improving health knowledge, behavioral outcomes, psychological well-being, and patient satisfaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA systematic search of the Cochrane Library, PubMed, CINAHL, MEDLINE, and EMBASE was conducted for randomized controlled trials published between 2014 and April 2025. Studies involving adults with chronic diseases and virtual nurse interventions reporting outcomes on knowledge, behavior, psychological well-being, or patient satisfaction were included. Methodological quality was appraised using the Joanna Briggs Institute checklist. Due to heterogeneity in interventions and outcome measures, data were synthesized narratively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSix trials with 533 participants were included. Virtual nurse interventions consistently improved disease-specific knowledge and psychological well-being, and were associated with high patient satisfaction. Evidence for behavioral adherence and clinical outcomes was limited and often not statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eVirtual nursing enhances knowledge, emotional support, and psychological well-being in chronic disease self-management. Its long-term effects on behavioral change and clinical outcomes remain unclear. Nurse engagement, organizational support, and attention to digital literacy are essential to optimize implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003ePROSPERO CRD420251038439.\u003c/p\u003e","manuscriptTitle":"Effectiveness of Virtual Nurse Interventions in Chronic Disease Self-Management: A Systematic Review of Randomized Controlled Trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 09:03:13","doi":"10.21203/rs.3.rs-8182311/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T10:36:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T13:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250107665428498996530785423640289460059","date":"2025-12-11T11:40:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T08:12:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66201038166350154749025643578719432134","date":"2025-12-11T08:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T07:55:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-02T12:25:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T11:54:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T11:54:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-11-22T19:36:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1cecaee-c61f-4381-866b-1af3980389ad","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:18:03+00:00","versionOfRecord":{"articleIdentity":"rs-8182311","link":"https://doi.org/10.1186/s12912-026-04479-1","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-03-02 15:58:05","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-12-16 09:03:13","video":"","vorDoi":"10.1186/s12912-026-04479-1","vorDoiUrl":"https://doi.org/10.1186/s12912-026-04479-1","workflowStages":[]},"version":"v1","identity":"rs-8182311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8182311","identity":"rs-8182311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}