Abstract
Objectives
This study aimed to explore nurses’ perceptions of, and utilization plans for, companion robots to support the physical and mental health of patients with acute stroke. In addition, the study sought to provide foundational data for the development of companion robots tailored to acute stroke patients. It also investigated obstructive factors and potential solutions to difficulties encountered when applying companion robots in the care of patients with acute stroke.
Methods
Using the Delphi technique, this study surveyed 14 nurses working in the neurology ward and stroke intensive care unit of a tertiary hospital in Seoul across three survey rounds.
Results
After completion of the three Delphi survey rounds, Cronbach’s α was 0.78, and stability values were all below 0.5; therefore, no additional rounds were conducted. A total of 54 items were finally selected, including 10 items related to educational aspects for nurses and patients, 12 items addressing impacts on nurses and patients, 19 items describing companion robot functions required for stroke patients, and 13 items identifying the most appropriate design elements.
Conclusions
Companion robots are expected to contribute to the physical and emotional care of patients with acute stroke admitted to tertiary hospitals by functioning as a nursing intervention, while also reducing nurses’ workload, improving the quality of nursing care, and supporting patient safety management. In addition, efforts should be made to ensure the harmonious control and utilization of newly developed robots and to strengthen robot-related job competencies among nurses.
Keywords: Robotics, Stroke, Nursing, Methods, Delphi Technique
I. Introduction
With the advent of the fourth industrial revolution, various changes have occurred across the health and medical fields [1,2]. In particular, recent advances in emerging technologies, such as the Internet of Things, artificial intelligence, telemedicine and remote patient monitoring, 3D and 4D printing, robotics, and large language models, have driven remarkable transformations in healthcare. These innovations have revolutionized diagnostics, treatment, and patient monitoring by enabling more precise disease detection, personalized care delivery, and improved efficiency and accessibility of healthcare services [2].
Robots in nursing have been actively developed for diverse purposes, including assistance with activities of daily living, support for patient mobility and logistics, promotion of social interaction and emotional stability, reduction of nurses’ physical workload, minimization of errors, and enhancement of the overall quality and safety of patient care [3]. A previous study has shown that interventions using personal assistive robots among older adults with mild dementia significantly improved cognitive function and mental well-being, while also reducing depression and loneliness, with transient improvements observed in autonomic nervous system activity during the intervention period [4]. Furthermore, the application of care robots for patients with type 2 diabetes mellitus and cognitive impairment has demonstrated significant benefits, including reduced depression and anxiety, improved sleep quality, and enhanced medication adherence [5].
Patients recovering from stroke often experience a range of physical impairments, including hemiplegia, gait disturbance, dysarthria, and cognitive impairment, as well as psychological sequelae such as depression, anxiety, and stress [6,7]. Based on prior studies examining nursing robot applications and stroke-related conditions, the use of companion robots for patients with acute stroke is expected to support cognitive function and mood stabilization. In addition, companion robots may enhance activities of daily living and quality of life, which are factors known to positively influence recovery outcomes [8]. However, most existing studies on robot applications have been conducted in nursing homes or community-based daycare centers, and research examining the application of robots in patient care within tertiary hospital settings remains insufficient.
Therefore, this study aims to examine nurses’ perceptions and utilization plans regarding the implementation of companion robots for patients with acute stroke. Furthermore, the study seeks to identify essential functional and design requirements for companion robot development and to explore obstructive factors and potential solutions related to their application using the Delphi technique. Based on these findings, this study also aims to develop a strategy for applying companion robots to acute stroke patient care through a strengths, weaknesses, opportunities, and threats (SWOT) analysis.
II. Methods
1. Research Design
This Delphi research study collected opinions from an expert panel to reach consensus on “applying companion robots to acute stroke patients” through three sequential stages. The overall procedure used in this study is illustrated in Figure 1.
Figure 1.
Research procedure. SWOT: strength, weakness, opportunity, threat.
2. Delphi Panel
For this study, 14 nurses with more than seven years of experience working in a tertiary hospital’s neurology ward and stroke intensive care unit were selected as the expert panel. Benner classified nurses with more than seven years of experience as “experts” through the model of skill acquisition [9], and according to Ewing [10] and Adler and Ziglio [11], a panel consisting of 10–15 experts is considered sufficient for conducting Delphi research [10,11]. Accordingly, six nurses from the neurology ward and eight nurses from the stroke intensive care unit were recruited as the expert panel. The experts were shift workers with varying schedules, which prevented direct interaction during the study period. In addition, participants were instructed not to discuss the content of their responses with one another.
3. Survey Questionnaire
The first Delphi open-ended questionnaire was developed based on a review of previous studies on robot use in nursing and nurses’ perceptions of such technologies. The questionnaire included items addressing “demographic characteristics of participants,” “perceptions and utilization plans for applying companion robots,” and “obstructive factors and solutions related to applying companion robots.” In addition, it included questions regarding “companion robots’ function and design” to be considered according to nursing problems and the age of patients with acute stroke (Supplement A).
For the second Delphi round, the expert opinions collected from the first open-ended questionnaire were reorganized into closed-ended questions. The second Delphi questionnaire was structured into four domains: “nurse education aspect,” “patient education aspect,” “impact of companion robot application on nurses and patients,” and “functions and designs necessary for companion robot development.” For the third Delphi round, the questionnaire was revised by incorporating newly added opinions, and items identified as low priority were removed based on the results of the second Delphi survey.
4. Data Collection
The Delphi survey was conducted from November 7, 2022, to April 12, 2023. In the first round, participants were asked to refer to scenarios or assumptions about the future presented in previous studies and to respond accordingly. In the second round, participants evaluated the appropriateness of each item using a 5-point Likert scale, with 5 indicating the highest adequacy and 1 indicating the lowest adequacy. An open comment section was provided to allow participants to revise and supplement their responses, thereby addressing limitations of the closed-ended questionnaire. The third round followed the same 5-point Likert scale used in the second round. Each expert was presented with their individual responses from the second round along with corresponding statistical results, including the mean, standard deviation, median, and quartile range. Responses in the third round were guided to remain consistent with or diverge from those of the second round. When third-round responses fell outside the quartile range, representing the central 50% of responses based on the second-round average, participants were required to provide a rationale for their differing opinions. This process enabled experts to review their previous responses and reconsider their answers.
5. Data Analysis
1) Delphi first round
The results of the first Delphi survey were reconstructed into closed-ended questions. Education related to the application of companion robots was divided into nurse-related and patient-related components. The perceived impacts on nurses and patients were further classified into positive and negative aspects. Finally, the functions and designs of companion robots required for patients with acute stroke were reorganized into closed-ended questionnaire items.
2) Delphi second round
For the second Delphi survey, the mean, standard deviation, median, and quartile range of responses were calculated using SPSS version 21.0 (IBM, Armonk, NY, USA). Content validity was assessed using the content validity ratio (CVR), and consensus and convergence were calculated using Excel to determine whether expert opinions had reached agreement. Content validity was considered acceptable when the minimum CVR value was 0.51 or higher [12], consensus was 0.75 or higher, and convergence ranged between 0 and 0.5 [13,14].
3) Delphi third round
As in the second Delphi round, the mean, standard deviation, median, quartile range, CVR, consensus, and convergence of responses from the third Delphi survey were calculated. To assess the reliability of the questionnaire, Cronbach’s α was calculated, with values of 0.7 or higher considered acceptable [15]. Stability was then evaluated using the coefficient of variation to determine consistency among expert responses. A coefficient of variation of 0.5 or less was considered indicative of stability [16].
4) SWOT analysis
Based on the results of the third Delphi round, the researchers conducted a SWOT analysis. This sequential integration of Delphi and SWOT analyses has been adopted in previous healthcare research to identify and prioritize strategic factors [17,18], supporting the methodological appropriateness of the present study design.
In the SWOT analysis, “Strength” was defined as elements and capabilities of the hospital and medical staff, while “Weakness” referred to institutional obstacles and insufficient competencies among medical personnel. “Opportunity” was defined as positive factors within the social environment, and “Threat” was defined as external factors that could act as obstacles or pose potential risks. After constructing the SWOT matrix, the results were analyzed by categorizing strategies into four types: strength–opportunity (SO), strength–threat (ST), weakness–opportunity (WO), and weakness–threat (WT) [19].
6. Ethical Considerations
This study was conducted following approval from the Institutional Review Board (Approval No. KC22QISI0756). All participants provided written informed consent after receiving a full explanation of the study’s purpose, duration, and methods; the questionnaire response process; their right to withdraw consent at any time; and assurances regarding confidentiality.
III. Results
1. Delphi Study
In the first round of the Delphi study, nurses’ perceptions regarding the use of companion robots and education related to their use were investigated, along with plans for their application in tertiary hospitals. In addition, essential functions required for the development of companion robots for patients with acute stroke were examined, together with design considerations and obstructive factors and potential solutions for introducing companion robots. The detailed questions included in the first round and the consensus processes applied in the second and third rounds are presented in Supplement A.
This process achieved consensus among experts through three rounds of the Delphi survey. Reliability was confirmed with a Cronbach’s α of 0.78, and the stability of all items was verified to be below 0.5. Accordingly, the survey was concluded without conducting additional rounds. A total of 54 final items related to nurses’ perceptions and utilization plans for applying companion robots to patients with acute stroke were selected (Supplement B). The main findings of the study are summarized below.
When analyzing the use of companion robots, the most appropriate educational type and method for nurses were identified as a “continuing education program” and “simulation,” respectively (Table 1). Educational content included “functions of companion robots,” “usage of companion robots,” “measures for malfunctions,” and “previous studies.” For patients, the most appropriate educational types and methods were identified as “individual” and “small group” formats, as well as “demonstration and hands-on practice” and “individualized face-to-face education.” Positive impacts of introducing companion robots for nurses included “increased work efficiency” and “prevention of burnout due to work overload,” whereas negative impacts included the “addition of patient training work for robot use” and the “addition of new tasks related to robot management” (Table 2). Positive impacts on patients included “enhanced medication adherence,” “information provision,” “emotional support,” “alleviation of psychological burden,” and “early rehabilitation,” while negative impacts included “interference with patient rest,” “overburden of stress,” and “challenges related to charging and maintenance.”
Table 1.
Educational aspects of nurses and patients
| Subject | Category | Sub-category | Details |
|---|---|---|---|
| Nurse | Educational type | Continuing education program | Structured and regular training sessions designed to equip nurses with the latest knowledge and deepen their understanding of companion robots |
| Method | Simulation | Practical training that replicates real-world clinical scenarios, preparing nurses to effectively utilize robots in healthcare settings, including troubleshooting. | |
| Content | Functions of companion robots | Detailed explanations of the robot’s features tailored to patient needs, such as emotional support and safety monitoring. | |
| Usage of companion robots | Education for nurses on the operation of basic functions, including power on/off, charging, volume adjustment, and screen settings. | ||
| Measures for malfunctions | Training for nurses on protocols for identifying and addressing robot malfunctions to ensure continuous operation. | ||
| Previous studies | Examination of prior research and case studies to illustrate the implementation and impact of companion robots in healthcare. | ||
| Patient | Educational type | Individual | Customized education sessions tailored to each patient’s condition, focusing on their specific needs and abilities. |
| Small group | Small group sessions to promote the use of companion robots by delivering consistent educational content to multiple patients simultaneously. | ||
| Method | Demonstration and hands-on practice | Live demonstrations of the robot’s functions followed by hands-on practice, enabling patients to build confidence in its use. | |
| Individualized face-to-face education | Direct and personalized instruction designed to ensure patients fully understand and feel comfortable using the robot. |
Table 2.
Impacts on nurses and patients
| Subject | Impact type | Category | Details |
|---|---|---|---|
| Nurse | Positive | Increased work efficiency | Guidance on diagnostic tests and rehabilitation schedules while providing explanations about stroke to patients. |
| Prevention of burnout due to work overload | Provision of treatment information and emotional support to reduce the frequency of nurse calls, alleviating workload. | ||
| Negative | Addition of patient training work for using robots | Requirement for additional tasks, such as explaining how to use the robot to patients and addressing their questions or concerns. | |
| Addition of new tasks for robot management | Requirement to resolve issues related to robot charging, malfunctions, errors, and loss. | ||
| Patient | Positive | Enhanced medication adherence | Repeated medication alarms, ensuring patients take their medications correctly at the prescribed times. |
| Information provision | Repeated explanations about stroke diagnostic tests and treatment, improving patients’ understanding of their condition and enhancing treatment outcomes. | ||
| Emotional support | Relief of patients’ anxiety, mood swings, and stress through communication with robots in unfamiliar hospital environments. | ||
| Alleviation of psychological burden | Ability for patients to share sensitive sequelae, such as dysarthria or hemiplegia, with robots without fear of judgment from others. | ||
| Early rehabilitation | Delivery of diverse stimuli at the bedside to encourage active engagement in rehabilitation activities. | ||
| Negative | Interference with patient rest | Unintentional stimulation of patients at inappropriate times, potentially disrupting rest. | |
| Overburden of stress | Errors in robotic functions that may cause patients stress and difficulty in resolving issues. | ||
| Challenges in charging and maintenance | Potential burden on patients due to the unfamiliarity and complexity of managing robotic devices. |
The companion robot functions required for patients with stroke were identified as “rehabilitation function,” “information provision,” “emotional support,” “promotion of patient safety,” and “others” (Table 3). Materials for companion robots were identified to reduce “infection risk,” “damage risk,” and “sores,” while also emphasizing features such as “lightweight” construction and “fabric” materials (Table 4). Design considerations included “brightness,” “chroma,” “safety,” and “mobility.” Weight categories were finalized as “less than 1 kg” and “between 1 kg and less than 3 kg,” and preferred appearances were identified as “animal” and “character” designs.
Table 3.
Companion robot functions required for stroke patients
| Category | Sub-category | Details |
|---|---|---|
| Rehabilitation function | Speech and language therapy | Enables patients to practice speech by reading words on the robot’s screen and singing along with the robot. |
| Exercise therapy, occupational therapy | Provides images for exercises and occupational therapy that can be performed in bed, complementing treatments conducted in the therapy room. | |
| Orientation training | Provides periodic reminders about the time, location, weather, season, and inpatient treatment status. | |
| Information provision | Medication reminder | Notifies patients when to take antithrombotic drugs, emphasizing the importance of maintaining proper blood concentration. |
| Enteral feeding education | Provides instructions for nasogastric tube feeding, including steps and precautions. | |
| Discharge education | Educates patients on precautions and guidance for returning to daily life after discharge. | |
| Stroke diagnostic test guidance | Provides instructions on the timing, procedures, and precautions for stroke diagnostic tests. | |
| Stroke education | Educates patients and caregivers about neurological symptoms of stroke exacerbation and the importance of promptly notifying medical staff. | |
| Medication information | Provides guidance on the efficacy, effectiveness, and side effects of prescribed medications. | |
| Therapeutic diets | Provides explanations about therapeutic diets, such as dysphagia diets and dental assistance diets, as well as instructions on the use of thickening agents. | |
| Emotional support | Emotional support | Provides emotional support by reading positive texts, conducting quizzes, playing music, engaging in conversations, reading religious words, and displaying family photos on the screen. |
| Promoting patient safety | Fall monitoring | Notifies when bedside rails are not applied in the absence of a guardian or when the patient attempts to move alone. |
| Surrounding situation guidance | Guides patients with vision loss by identifying surrounding obstacles and objects to prevent potential harm. | |
| Prevention of aspiration risk | Notifies nurses in case of coughing during medication administration or meals. | |
| Real-time patient location tracking | Tracks the patient’s movements in real time during walks or outings when accompanied by the robot. | |
| Others | Volume adjustment Screen text size adjustment | Allows volume adjustment to accommodate the hearing abilities of elderly patients. Allows text size adjustment to suit the vision abilities of elderly or visually impaired patients. |
| Slow and repeated notification | Provides slow and repetitive notifications to ensure understanding for elderly or cognitively impaired patients. | |
| Call function | Facilitates voice and video calls with family members when the resident caregiver is not a family member. |
Table 4.
Companion robot designs appropriate for stroke patients
| Category | Sub-category | Details |
|---|---|---|
| Material | Prevention of infection risk | Washable cover that allows cleaning with disinfectants to minimize infection risk. |
| Prevention of damage risk | Construction with unbreakable materials to prevent damage if dropped due to poor handgrip strength. | |
| Prevention of sores | Soft, fluffy materials to prevent pressure sores. | |
| Lightweight | Design incorporating lightweight materials to accommodate the condition of patients with limb weakness. | |
| Fabric | Use of fabric materials instead of metal to prevent discomfort from extreme temperatures for patients with decreased sensation. | |
| Design | Consideration of brightness | Clear contrasts in brightness to accommodate patients with visual impairments. |
| Consideration of chroma | Low chroma and minimal color differences to reduce dizziness and headaches. | |
| Consideration of safety | Rounded corners to prevent bleeding, especially for patients taking antiplatelet medications. | |
| Consideration of mobility | Arm and leg movements to demonstrate joint mobility. | |
| Weight | Less than 1 kg | - |
| Between 1 kg and less than 3 kg | - | |
| Appearance | Animal (cat, dog, bear, rabbit, etc.) | - |
| Character | - |
2. SWOT Analysis
Increased experience and interest encountered daily by patients, guardians, and medical staff were identified as strengths, as these factors facilitated rapid adaptation to new environments through educational approaches such as simulation-based training. In addition, technological advancements enabling robots to perform various roles were considered strengths because they improved nurses’ work efficiency and contributed to the prevention of burnout.
Weaknesses were identified as increased fatigue related to familiarity and management demands, which were attributed to limited experience with medical robots and the lack of dedicated personnel and structured training. In addition, the potential risk of trauma occurring during patient use of robots was considered a weakness.
Opportunities were identified in the potential for developing diverse types of robots through publicity and technological advancement using various media platforms. Furthermore, increasing interest among researchers and academic societies in the positive effects of nursing robots was recognized as an opportunity. The potential introduction of health insurance coverage for robot applications and their consideration as public institution projects were also analyzed as opportunities.
Threats were identified as difficulties in covering costs when health insurance is not applied and challenges related to meeting the individualized needs of diverse patients. In addition, the lack of regulations and education regarding robot management and application, as well as limitations in nursing care areas that require uniquely human interaction, were analyzed as threats. Based on these findings, a companion robot application strategy for patients with acute stroke was derived according to the SO, WO, ST, and WT strategies (Figure 2).
Figure 2.
Strategies for applying companion robots for acute stroke patients.
IV. Discussion
This Delphi study was conducted to identify nurses’ perceptions and plans regarding the application of companion robots for patients with acute stroke. It is meaningful in that it provides foundational data necessary for the development of companion robots specifically designed for stroke patients. In addition, a SWOT analysis was employed to derive strategic plans for introducing companion robots tailored to the environments of tertiary hospitals and the characteristics of stroke patients.
Based on the results of this study, nurses encountered various robots in their daily lives but did not actively use or study robots for nursing practice. This tendency is consistent with findings from a qualitative study indicating that limited exposure and insufficient training among nurses are major barriers to adopting emerging technologies, such as artificial intelligence, in clinical practice. These findings emphasize the need for structured and continuous education to strengthen nurses’ technological competence [20]. Therefore, to expand nurses’ experience with nursing robots, effective curriculum development and simulation-based training programs should be implemented, along with promotional activities utilizing various media.
In addition, the expert panel agreed that 19 items were appropriate as required functions for companion robots designed for stroke patients. Among these, several information-providing functions were identified as particularly appropriate, including “enteral feeding education,” “discharge education,” “stroke diagnostic test guidance,” and “stroke education.” These findings are consistent with previous research highlighting the importance of information-provision functions, such as social robots delivering dementia-related support information to family caregivers through alarm functions [21].
Regarding design considerations, the most appropriate items identified were “prevention of infection and damage risk,” “consideration of brightness and chroma of robot colors,” and “consideration of safety and mobility.” Accordingly, standard safety guidelines, such as the use of rounded edges and assessment of noise tolerance to prevent harm during care robot use, should be established during robot development [22]. This is particularly important because these robots are intended for use by patients, caregivers, and medical staff rather than by robot experts. Furthermore, the development of companion robot technology requires a multidisciplinary approach that incorporates the perspectives of robot engineers, patients, guardians, medical personnel, and hospital administrators responsible for introducing such technologies.
The primary obstacles to the commercialization of companion robots in tertiary hospitals were identified as “cost problems,” “concerns about reduced interaction with medical staff,” “difficulty of use for older adults or patients with cognitive impairment,” and “risk of damage resulting from robot malfunction.” These concerns are consistent with recent evidence indicating that nurses express substantial apprehension regarding robot use in clinical care, particularly related to the risk of dehumanized nursing due to reduced empathy and interaction, as well as potential patient harm arising from malfunction and reliability issues [23].
Therefore, to increase robot utilization among older adults and patients with difficulty using technology, it is necessary to consider factors such as ease of operation, battery charging, size, and design. Robots should be developed with careful consideration of patients’ disease characteristics, age, and individual needs. In addition, short and repetitive training sessions are needed to reflect the limited attention span of older adults and patients with cognitive dysfunction. Moreover, hospital-level regulations should be established regarding robot application, taking into account various scenarios, including responsibility and patient harm in cases of robot malfunction. Attention should also be directed toward addressing cost-related issues by supporting medical staff, reviewing health insurance applications, and exploring expansion into public institution projects.
This study has several limitations. First, the findings may not be generalizable to all nurses caring for stroke patients because the expert panel consisted solely of nurses working in a tertiary hospital. It is difficult to exclude the possibility that the working environment or institutional characteristics of a specific hospital influenced the results, as well as the potential for bias arising from convenience sampling during expert selection. In addition, although participants were instructed not to discuss their Delphi responses in advance, it cannot be completely ruled out that communication among experts occurred during the study period. Therefore, future research should consider strategies for applying companion robots while accounting for differences in perceptions across diverse occupations and care settings for stroke patients.
Furthermore, due to the nature of Delphi research, this study explored uncertainty related to companion robot application by collecting expert opinions. As a result, there remains the possibility of future changes in perceptions, as well as transmission errors associated with non–face-to-face communication among expert panel members. Nevertheless, this study is meaningful in identifying trends in nurses’ perceptions and usability of companion robots and in presenting a preliminary blueprint for understanding application flow.
Based on the findings of this study, the successful application of companion robots for stroke patients admitted to tertiary hospitals may support nurses who are unable to remain continuously at the patient’s bedside for 24 hours a day, thereby providing multifaceted benefits during hospitalization. In addition to supporting physical function recovery, companion robots may interact with patients and contribute to the maintenance and improvement of emotional stability and cognitive function, positioning them as a non-pharmacological intervention. These effects may reduce nurses’ workload, enhance the quality of patient care, and support patient safety management. Furthermore, the study is expected to contribute to advancements in robotics, science, and technology by serving as a starting point for future research on companion robot functions and designs applicable to patients with various diseases in tertiary hospital settings.
Based on the above findings, the researchers present the following suggestions. First, we propose further research on the development and effectiveness of a systematic curriculum for robot use in universities and hospitals. Educational needs among nurses responsible for education should be assessed, and customized training should be provided based on their level of knowledge and experience with robotic technologies. Second, we propose conducting repeated studies to collect additional expert opinions by expanding the expert panel to include participants from various hospitals. Such studies should involve community nurses, gerontological nurse practitioners, and rehabilitation therapists with experience in nursing robotics care to facilitate more systematic and multidisciplinary discussions regarding application strategies. Third, we propose additional research to examine the perspectives of patients and guardians who will directly use companion robots. These users should be able to learn and operate robots easily, and efforts to motivate acceptance and sustained use of new technologies should be prioritized. Finally, it is necessary to consider how nurses can smoothly control and utilize emerging robotic technologies in clinical practice. Active engagement with the evolving hospital environment is required, along with sustained efforts to strengthen and develop nurses’ job competencies related to these technologies.
Footnotes
Conflict of Interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
This article is a revision of the first author’s master’s thesis from The Catholic University of Korea.
Supplementary Materials
Supplementary materials can be found via https://doi.org/10.4258/hir.2026.32.1.59.
References
- 1.Gibelli F, Ricci G, Sirignano A, Turrina S, De Leo D. The increasing centrality of robotic technology in the context of nursing care: bioethical implications analyzed through a scoping review approach. J Healthc Eng. 2021;2021:1478025. doi: 10.1155/2021/1478025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Thacharodi A, Singh P, Meenatchi R, Tawfeeq Ahmed ZH, Kumar RR, VN, et al. Revolutionizing healthcare and medicine: the impact of modern technologies for a healthier future: a comprehensive review. Health Care Sci. 2024;3(5):329–49. doi: 10.1002/hcs2.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Adeyemo A, Coffey A, Kingston L. Utilisation of robots in nursing practice: an umbrella review. BMC Nurs. 2025;24(1):247. doi: 10.1186/s12912-025-02842-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Chen SC, Lin MF, Jones C, Chang WH, Lin SH, Chien CO, et al. Effect of a group-based personal assistive robot (PARO) robot intervention on cognitive function, autonomic nervous system function, and mental well-being in older adults with mild dementia: a randomized controlled trial. J Am Med Dir Assoc. 2024;25(11):105228. doi: 10.1016/j.jamda.2024.105228. [DOI] [PubMed] [Google Scholar]
- 5.Kim YI, Lee HW, Kim TH, Kim JH, Ok KI. The effect of care-robots on improving anxiety/depression and drug compliance among the elderly in the community. J Korean Soc Biol Ther Psychiatry. 2020;26(3):218–26. doi: 10.22802/jksbtp.2020.26.3.218. [DOI] [Google Scholar]
- 6.Lee WJ, Lee JW. The effect of stress and sense of coherence on health behavior compliance in outpatients with stroke. J Korea Acad Ind Coop Soc. 2020;21(1):232–9. doi: 10.5762/KAIS.2020.21.1.232. [DOI] [Google Scholar]
- 7.Choi YY, Lee M, Kim ES, Lee JW, Kwon YI, Ha YM. Effects of music therapy on depression and rehabilitation motivation of inpatients with stroke. J Converg Inf Technol. 2021;11(4):220–9. doi: 10.22156/CS4SMB.2021.11.04.220. [DOI] [Google Scholar]
- 8.Eun Y. Geriatric nursing and robotic nurses. Korean J Res Gerontol. 2018;27(2):111–8. doi: 10.25280/kjrg.27.2.4. [DOI] [Google Scholar]
- 9.Benner P. From novice to expert. Am J Nurs. 1982;82(3):402–7. [PubMed] [Google Scholar]
- 10.Ewing DM. Future competencies needed in the preparation of secretaries in the state of Illinois using the Delphi technique [dissertation] Urbana (IL): University of Illinois at Urbana-Champaign; 1991. [Google Scholar]
- 11.Adler M, Ziglio E. Gazing into the oracle: the Delphi method and its application to social policy and public health. London, UK: Jessica Kingsley Publishers; 1996. [Google Scholar]
- 12.Lawshe CH. A quantitative approach to content validity. Pers Psychol. 1975;28(4):563–75. doi: 10.1111/j.1744-6570.1975.tb01393.x. [DOI] [Google Scholar]
- 13.Lee HG, Lee Y. The development of an assessment framework for technological problem solving capability. J Agric Educ Human Resour Dev. 2006;38(4):33–61. [Google Scholar]
- 14.Lee G, Jyung C. The development of college major selection program model for high school students. J Agric Educ Human Resour Dev. 2009;41(1):87–110. doi: 10.23840/agehrd.2009.41.1.87. [DOI] [Google Scholar]
- 15.Cronbach LJ, Gleser GC, Nanda H, Rajaratnam N. The dependability of behavioral measurements: theory of generalizability for scores and profiles. New York (NY): John Wiley & Sons Inc; 1972. [Google Scholar]
- 16.Rho SY. Delphi technique: future forecasting with professional insights. Plan Policy. 2006;299:53–62. [Google Scholar]
- 17.Pietrantonio F, Rosiello F, Alessi E, Pascucci M, Rainone M, Cipriano E, et al. Burden of COVID-19 on Italian internal medicine wards: Delphi, SWOT, and performance analysis after two pandemic waves in the local health authority “Roma 6” hospital structures. Int J Environ Res Public Health. 2021;18(11):5999. doi: 10.3390/ijerph18115999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dim CC, Mbachu CO, Enebe NO, Kassy WC, Nnagbo JE, Obi IE, et al. Strategic SWOT analysis of the University of Nigeria Teaching Hospital using Modified Delphi Technique: implications for strengthening national and regional health systems. BMC Health Serv Res. 2025;25(1):35. doi: 10.1186/s12913-024-12076-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Joe IH. The SWOT analysis. Gyeongju, Korea: Modi-Books; 2019. [Google Scholar]
- 20.Ramadan OM, Alruwaili MM, Alruwaili AN, Elsehrawy MG, Alanazi S. Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses’ perspectives. BMC Nurs. 2024;23(1):891. doi: 10.1186/s12912-024-02571-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kim SK, Hwang YS, Jang JW, Jo HS. Evaluation of the effectiveness of social robot by family caregivers nursing for the older adult with cognitive impairment: a randomized controlled trial. J Korean Gerontol Nurs. 2022;24(2):142–50. doi: 10.17079/jkgn.2022.24.2.142. [DOI] [Google Scholar]
- 22.Ahn S, Moon I. A study on standard for safety requirements for care robots. Stud Health Technol Inform. 2023;306:57–62. doi: 10.3233/SHTI230596. [DOI] [PubMed] [Google Scholar]
- 23.El-Gazar HE, Abdelhafez S, Ali AM, Shawer M, Alharbi TA, Zoromba MA. Are nurses and patients willing to work with service robots in healthcare? A mixed-methods study. BMC Nurs. 2024;23(1):718. doi: 10.1186/s12912-024-02336-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
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