The determinants of home healthcare robots adoption: An empirical investigation

An investigation into the acceptance of intelligent care systems: an extended technology acceptance model (TAM)

With the aging population trend becoming increasingly pronounced, the health issues of elderly individuals living alone have become a focal point of societal concern. This study aims to investigate guardians of the elderly's acceptance of intelligent care systems for the elderly. This system integrates millimeter-wave radar and image recognition technologies to monitor the health status of seniors in real time and automatically alert their children in emergency situations. To evaluate the market acceptance of this emerging technology, we employed a Covariance-Based Structural Equation Modeling (CB-SEM) approach and constructed an acceptance model for the intelligent care system. Survey data were collected from 386 respondents in China. The results indicate that users of this system are more concerned with task completion rather than ease of use. Enhancements in information trust significantly promote perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention to use (BI). Individuals with higher risk perception sensitivity exhibit greater perceptions of the system's usefulness and ease of use. Aesthetics emerged as a significant factor influencing PU, PEOU, and BI, second only to information trust. When the system is perceived as well-designed, it is also deemed acceptable. An aesthetically pleasing system is not only considered useful but also easier to use. Interestingly, opinions from social circles did not directly impact BI or PEOU. they only influenced perceived usefulness. Moreover, higher privacy security requirements correlate with lower perceptions of the system's usefulness. Overall, improvements in perceived usefulness, information trust, and aesthetics significantly enhance user acceptance of the system. These findings provide theoretical support for developing more appealing intelligent care systems for the elderly and contribute new perspectives on understanding the key factors driving the adoption of such systems. Additionally, they enrich and refine the knowledge base within the TAM framework.

Determinants of Telehealth Adoption Among Older Adults: Cross-Sectional Survey Study

BACKGROUND

The aging population and the accompanying rise in chronic diseases have intensified the need to study the adoption of telehealth services. However, the success of telehealth services depends not only on their ease and usefulness but also on addressing broader concerns. Despite being a substantial user group in traditional health services, older adults may encounter barriers to adopting telehealth services. Increasing the adoption of telehealth among the older adult population is crucial for enhancing their access to care and managing the challenges of aging effectively.

OBJECTIVE

We aimed to explore factors influencing the adoption of telehealth services among older adults in Malaysia, going beyond the conventional framework by incorporating transition cost and subjective well-being as additional constructs.

METHODS

A cross-sectional survey was conducted among 119 adults aged ≥60 years in Malaysia, using 39 survey items adapted from existing studies. Data analysis was performed using partial least squares structural equation modeling, with both the measurement model and structural model being evaluated. To determine the predictive relevance of the model, PLSpredict was applied. In addition, importance-performance map analysis was conducted to further expand on the structural model results by assessing the performance of each variable.

RESULTS

Of the 119 participants, 52 (43.7%) were women and 67 (56.3%) were men. The study found that subjective well-being (β=0.448; P<.001) was the most significant factor, followed by attitude (β=0.242; P<.001), transition cost (β=-0.163; P<.001), and perceived usefulness (β=0.100, P=.02) in influencing telehealth service intention. Furthermore, perceived ease of use (β=0.271; P<.001), availability (β=0.323; P<.001), subjective well-being (β=0.261; P<.001), and trust (β=0.156, P=.004) positively influenced perceived usefulness, while inertia (β=0.024, P=.22) did not. In addition, availability (β=0.420; P<.001) and subjective well-being (β=0.260; P<.001) were positively related to perceived ease of use, with inertia (β=-0.246; P<.001) having a negative impact. The importance-performance map analysis results showed that subjective well-being (importance=0.532) was the most crucial factor for older adult users, while availability (importance=70.735) had the highest performance in telehealth services.

CONCLUSIONS

This research underscores the importance of catering to the subjective well-being of older adults and optimizing the availability of telehealth services to encourage adoption, ultimately advancing health care accessibility and quality for this vulnerable demographic.

Attitudes of support people: a key element when implementing technologies for people with intellectual and visual disabilities

AIM

The degree to which people with intellectual and visual disabilities can use technology relies on the level of support they receive. However, there is a lack of knowledge about the relationship between the constructs effort expectancy, attitudes, and behavioural intentions of support people (i.e. relatives and healthcare professionals) regarding the use of such technologies for people with intellectual and visual disabilities. The aim of this study was to gain insight into how these constructs are connected and to explore their relationship with support person's characteristics and the support need levels of people with intellectual and visual disabilities.

METHODS

In total, 186 support people from a Dutch healthcare organisation focusing on people with intellectual and visual disabilities participated in an online questionnaire. We used a regression analysis to explore how the constructs, the characteristics, and the level of support needs were related.

RESULTS

Both effort expectancy (β = .35; t(185) = 5.04; p < .001) and attitude (β = .75; t(185) = 15.55; p < .001) of support people were related to the intention to use technologies. The effect of effort expectancy (β = .04; t(177) = .74; p = .462) on the intention to use technologies was mediated through attitude (β = .74; t(177) = 13.28; p < .001). Younger support people scored higher on attitude than older support people.

CONCLUSIONS

Support people's effort expectancy and attitude play a significant role in their intention to use technologies when supporting people with intellectual and visual disabilities, with attitude emerging as pivotal factor.

Understanding adoption of mobility assistive products: a systematic review

Objective: A systematic review of 30 full-text articles was conducted on user acceptance of technologies that assist people who have functional difficulties (FD) with activities of daily living (ADLs) and mobility. Our objective was to better understand the adoption of mobility assistive products (mobAP) among people with FD.

Methods: A literature search in EBSCO, PubMed, SCOPUS, and Web of Science yielded 423 articles. Ultimately, thirty articles met our inclusion criteria.

Results: Four overarching themes were identified using a thematic analysis approach. The themes were Empowerment Through the Use of Mobility Assistive Products, Incorporating User-Centered Design, Understanding the Challenges Users Encounter, and Improvements to Research Design. These themes highlight trends across studies that indicate that mobility assistive products have the potential to improve the well-being of people with FD. Still, significant efforts need to be made to improve the training with these devices. Moreover, researchers, healthcare providers, and designers should carefully consider the environmental, social, and facilitating conditions that moderate acceptance of these products.

Conclusion: This work advocates for more inclusive research design methodologies based on the limitations of the included studies. Ultimately, this review highlights the importance of understanding mobility assistive product acceptance among people with FD. By synthesizing the literature and identifying knowledge gaps, this review informs evidence-based decision-making for healthcare professionals, policymakers, and designers to develop user-centered healthcare assistive products, training, and assessments.

A comparative study to elucidate factors explaining willingness to use home-care robots in Japan, Ireland, and Finland

The implementation of home-care robots is sometimes unsuccessful. This study aimed to explore factors explaining people's willingness to use home-care robots, particularly among care recipients and caregivers. Surveys were conducted in Japan, Ireland, and Finland. The survey questionnaire comprised four categories (familiarity with robots, important points about home-care robots, functions expected from home-care robots, and ethically acceptable uses), with 48 items assessing users' willingness to use home-care robots. The responses from 525 Japanese, 163 Irish, and 170 Finnish respondents were analyzed to identify common and distinct factors influencing their willingness to use these robots. Common factors across the countries included "willingness to participate in research and development," "interest in robot-related news," and "having a positive impression of robots". The distinct factors for each country were: "convenience" in Japan; "notifying family members and support personnel when an unexpected change occurs in an older person" in Ireland; and "design" in Finland. Therefore, developers should determine potential users' willingness to participate in the research and development of home-care robots and consider a system that involves them in the development process.

Adoption of Artificial Intelligence-Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review

BACKGROUND

Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)-enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots.

OBJECTIVE

This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes?

METHODS

We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings.

RESULTS

This review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training.

CONCLUSIONS

Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-doi:10.1136/bmjopen-2023-075278.

Factors Associated with Intention to Use Telerehabilitation for Children with Special Needs: A Cross-Sectional Study

Background: Children with special health care needs (CSHCN) require long-term and ongoing rehabilitation interventions supporting their development. Telerehabilitation can provide continuous rehabilitation services for CSHCN. However, few studies have explored the intention of CSHCN and their caregivers to use telerehabilitation and its impact on them. Objective: The objective of this study was to identify factors that influence the intention to use telerehabilitation among CSHCN and their caregivers. Methods: This study was a cross-sectional study. Based on the unified theory of acceptance and use of technology, extended with additional predictors (trust and perceived risk [PR]), this study developed a research model and proposed 10 hypotheses. A structured questionnaire was distributed to 176 caregivers. Data were analyzed and research hypotheses were tested using partial least squares structural equation modeling to better understand the factors influencing the use of telerehabilitation. Results: A total of 164 valid questionnaires were collected. CSHCN and their caregivers were overall satisfied with this telerehabilitation medical service. The results of the structural model analysis indicated that social influence (SI), facilitating conditions (FC), and trust had significant effects on behavioral intention (BI) to use telerehabilitation, while the paths between performance expectancy (PE), effort expectancy (EE), and PR and BI were not significant. PE, EE, and SI had a significant effect on trust. Moreover, EE and SI had indirect effects on BI, with trust as the mediator. Conclusions: The results indicated that SI, FC, and trust are significant factors influencing CSHCN and their caregivers' use of telerehabilitation. Trust is also an important mediator for the intention and highly influenced by PE, EE, and SI.

Acceptance of a robotic system for nursing care: a cross-sectional survey with professional nurses, care recipients and relatives

BACKGROUND

The end-users' acceptance is a core concept in the development, implementation and evaluation of new systems like robotic systems in daily nursing practice. So far, studies have shown various findings concerning the acceptance of systems that are intended to assist people with support or care needs. Not much has been reported on the acceptance of robots that provide direct physical assistance to nurses in bedside care. Therefore, this study aimed to investigate the acceptance along with ethical implications of the prototype of an assistive robotic arm aiming to support nurses in bedside care, from the perspective of nurses, care recipients and their relatives.

METHODS

A cross-sectional survey design was applied at an early stage in the technological development of the system. Professional nurses, care recipients and relatives were recruited from a university hospital and a nursing home in Germany. The questionnaire was handed out following either a video or a live demonstration of the lab prototype and a subsequent one-to-one follow-up discussion. Data analysis was performed descriptively.

RESULTS

A total of 67 participants took part in the study. The rejection of specified ethical concerns across all the respondents was 77%. For items related to both perceived usefulness and intention to use, 75% of ratings across all the respondents were positive. In the follow-up discussions, the participants showed interest and openness toward the prototype, although there were varying opinions on aspects such as size, appearance, velocity, and potential impact on workload.

CONCLUSIONS

Regarding the current state of development, the acceptance among the participants was high, and ethical concerns were relatively minor. Moving forward, it would be beneficial to explore the acceptance in further developmental stages of the system, particularly when the usability is tested.

Referring physicians' intention to use hospital report cards for hospital referral purposes in the presence or absence of patient-reported outcomes: a randomized trial

PURPOSE

This study aims to determine the intention to use hospital report cards (HRCs) for hospital referral purposes in the presence or absence of patient-reported outcomes (PROs) as well as to explore the relevance of publicly available hospital performance information from the perspective of referring physicians.

METHODS

We identified the most relevant information for hospital referral purposes based on a literature review and qualitative research. Primary survey data were collected (May-June 2021) on a sample of 591 referring orthopedists in Germany and analyzed using structural equation modeling. Participating orthopedists were recruited using a sequential mixed-mode strategy and randomly allocated to work with HRCs in the presence (intervention) or absence (control) of PROs.

RESULTS

Overall, 420 orthopedists (mean age 53.48, SD 8.04) were included in the analysis. The presence of PROs on HRCs was not associated with an increased intention to use HRCs (p = 0.316). Performance expectancy was shown to be the most important determinant for using HRCs (path coefficient: 0.387, p < .001). However, referring physicians have doubts as to whether HRCs can help them. We identified "complication rate" and "the number of cases treated" as most important for the hospital referral decision making; PROs were rated slightly less important.

CONCLUSIONS

This study underpins the purpose of HRCs, namely to support referring physicians in searching for a hospital. Nevertheless, only a minority would support the use of HRCs for the next hospital search in its current form. We showed that presenting relevant information on HRCs did not increase their use intention.

Cardiology professionals' views of social robots in augmenting heart failure patient care

Aims

Social robots are arriving to the modern healthcare system. Whether patients with heart failure, a prevalent chronic disease with high health and human costs would derive benefit from a social robot intervention has not been investigated empirically. Diverse healthcare provider's perspectives are needed to develop an acceptable and feasible social robot intervention to be adopted for the clinical benefit of patients with heart failure. Using a qualitative research design, this study investigated healthcare providers' perspectives of social robot use in heart failure patient care.

Methods and results

Interdisciplinary healthcare providers from a tertiary care cardiac hospital completed a structured individual interview and a supplemental questionnaire. The framework method was used to analyse the qualitative data. Respondents (n = 22; saturation was reached with this sample; 77% female; 52% physicians) were open to using social robots to augment their practice, particularly with collecting pertinent data and providing patient and family education and self-management prompts, but with limited responsibility for direct patient care. Prior to implementation, providers required robust evidence of: value-added beyond current remote patient monitoring devices, patient and healthcare provider partnerships, streamlined integration into existing practice, and capability of supporting precision medicine goals. Respondents were concerned that social robots did not address and masked broader systemic issues of healthcare access and equity.

Conclusion

The adoption of social robots is a viable option to assist in the care of patients with heart failure, albeit in a restricted capacity. The results inform the development of a social robotic intervention for patients with heart failure, including improving social robot efficiencies and increasing their uptake, while protecting patients' and providers' best interest.

Developing a model to explain users' ethical perceptions regarding the use of care robots in home care: A cross-sectional study in Ireland, Finland, and Japan

To date, research on ethical issues regarding care robots for older adults, family caregivers, and care workers has not progressed sufficiently. This study aimed to build a model that universally explains the relationship between the use of care robots and ethical awareness, such as regarding personal information and privacy protection in home care. We examined data obtained from cross-sectional surveys conducted in Japan (n=528), Ireland (n=296), and Finland (n=180). We performed a confirmatory factor analysis by using responses to 11 items related to the ethical use of care robots. We evaluated the model based on the chi-square to degrees of freedom ratio, the comparative fit index, and the root mean square error of approximation. Subsequently, we compared the model with the Akaike's information criterion. Ten items were adopted in the final model. There were 4 factors in the model: 'acquisition of personal information', 'use of personal information for medical and long-term care', 'secondary use of personal information', and 'participation in research and development'. All factor loadings of the final model ranged between 0.63 and 0.92, which were greater than 0.6, showing that the factors had a high influence on the model. The final model was applied to each country; the fit was relatively good in Finland and poor in Ireland. Although the three countries have different geographies, cultures, demographics, and systems, this study showed that the impact of ethical issues regarding the use of care robots in home care can be universally explained by the same model.

Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study

BACKGROUND

Telemedicine has great potential for diabetes management. The COVID-19 pandemic has boosted the development of telemedicine. However, the factors influencing the behavioral intentions to use and use behaviors of telemedicine in patients with diabetes in China are not clear.

OBJECTIVE

We aimed to understand the determinants of behavioral intention to use telemedicine based on an extended Unified Theory of Acceptance and Use of Technology model and to identify demographic factors associated with telemedicine use in patients with diabetes in China.

METHODS

Patients with diabetes who are aged ≥18 years were surveyed from February 1 to February 7, 2023. We distributed the survey link in 3 WeChat groups including a total of 988 patients with diabetes from the outpatient department or patients discharged from Changsha Central Hospital. Structural equation modeling was used to understand the determinants of behavioral intention. A multivariate logistic regression analysis was used to identify the demographic factors associated with telemedicine use.

RESULTS

In total, 514 questionnaires were collected. Of the respondents, 186 (36.2%) were diagnosed with COVID-19. The measurement model showed acceptable reliability, convergent validity, discriminant validity, and data fit indices. The model explained 63.8% of the variance in behavioral intention. Social influence, performance expectancy, and facilitating conditions positively influenced behavioral intention (β=.463, P<.001; β=.153, P=.02; and β=.257, P=.004, respectively). Perceived susceptibility, perceived severity, and effort expectancy had no significant impact on behavioral intention (all P>.05). The overall use of telemedicine was 20.6% (104/514). After adjusting for the behavioral intention score, the multivariate regression analysis showed that age, education, and family income were associated with telemedicine use. Telemedicine use was higher in the 40 to 59 years and 18 to 39 years age groups than in the ≥60 years age group (odds ratio [OR] 4.35, 95% CI 1.84-10.29, P=.001; OR 9.20, 95% CI 3.40-24.88, P<.001, respectively). Telemedicine use was higher in the senior high school and the university and more groups than in junior high school education and less group (OR 2.45, 95% CI 1.05-5.73, P=.04; OR 2.63, 95% CI 1.11-6.23, P=.03, respectively). Patients with a higher family income used telemedicine more often than the patients who had an annual family income ≤¥10,000 (CNY ¥1=US $0.1398; ¥10,000-¥50,000 group: OR 3.90, 95% CI 1.21-12.51, P=.02; ¥50,000-¥100,000 group: OR 3.91, 95% CI 1.19-12.79, P=.02; >¥100,000 group: OR 4.63, 95% CI 1.41-15.27, P=.01).

CONCLUSIONS

Social influence, performance expectancy, and facilitating conditions positively affected the behavioral intention of patients with diabetes to use telemedicine. Young patients, highly educated patients, and patients with high family income use telemedicine more often. Promoting behavioral intention and paying special attention to the needs of older adult patients, patients with low income, and patients with low levels of education are needed to encourage telemedicine use.

User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study

BACKGROUND

With the rapid advancement of artificial intelligence (AI) technologies, AI-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have emerged as potential tools for various applications, including health care. However, ChatGPT is not specifically designed for health care purposes, and its use for self-diagnosis raises concerns regarding its adoption's potential risks and benefits. Users are increasingly inclined to use ChatGPT for self-diagnosis, necessitating a deeper understanding of the factors driving this trend.

OBJECTIVE

This study aims to investigate the factors influencing users' perception of decision-making processes and intentions to use ChatGPT for self-diagnosis and to explore the implications of these findings for the safe and effective integration of AI chatbots in health care.

METHODS

A cross-sectional survey design was used, and data were collected from 607 participants. The relationships between performance expectancy, risk-reward appraisal, decision-making, and intention to use ChatGPT for self-diagnosis were analyzed using partial least squares structural equation modeling (PLS-SEM).

RESULTS

Most respondents were willing to use ChatGPT for self-diagnosis (n=476, 78.4%). The model demonstrated satisfactory explanatory power, accounting for 52.4% of the variance in decision-making and 38.1% in the intent to use ChatGPT for self-diagnosis. The results supported all 3 hypotheses: The higher performance expectancy of ChatGPT (β=.547, 95% CI 0.474-0.620) and positive risk-reward appraisals (β=.245, 95% CI 0.161-0.325) were positively associated with the improved perception of decision-making outcomes among users, and enhanced perception of decision-making processes involving ChatGPT positively impacted users' intentions to use the technology for self-diagnosis (β=.565, 95% CI 0.498-0.628).

CONCLUSIONS

Our research investigated factors influencing users' intentions to use ChatGPT for self-diagnosis and health-related purposes. Even though the technology is not specifically designed for health care, people are inclined to use ChatGPT in health care contexts. Instead of solely focusing on discouraging its use for health care purposes, we advocate for improving the technology and adapting it for suitable health care applications. Our study highlights the importance of collaboration among AI developers, health care providers, and policy makers in ensuring AI chatbots' safe and responsible use in health care. By understanding users' expectations and decision-making processes, we can develop AI chatbots, such as ChatGPT, that are tailored to human needs, providing reliable and verified health information sources. This approach not only enhances health care accessibility but also improves health literacy and awareness. As the field of AI chatbots in health care continues to evolve, future research should explore the long-term effects of using AI chatbots for self-diagnosis and investigate their potential integration with other digital health interventions to optimize patient care and outcomes. In doing so, we can ensure that AI chatbots, including ChatGPT, are designed and implemented to safeguard users' well-being and support positive health outcomes in health care settings.

Factors That Influence the Use of eHealth in Home Care: Scoping Review and Cross-sectional Survey

BACKGROUND

In home care, eHealth implementation requires health care professionals and home care clients to change their behavior because they have to incorporate the use of eHealth into their daily routines. Knowledge of factors that influence the use of eHealth in home care is needed to optimize implementation strategies. However, a comprehensive overview of such factors is lacking.

OBJECTIVE

The aims of this study were to (1) provide insight into the types of eHealth that are used and preferred in home care and (2) identify factors that influence the use of eHealth in home care according to health care professionals and home care clients.

METHODS

A scoping review and online, cross-sectional survey were conducted sequentially. The survey was conducted among Dutch health care professionals with a nursing background who were working for a home care organization at the time. The capability, opportunity, motivation, behavior (COM-B) model, which posits that for any behavior (B) to occur, a person must have the capability (C), opportunity (O), and motivation (M) to perform the behavior, was used to identify influencing factors. The use of a theoretical model may contribute to a better understanding of how to achieve and sustain behavior change in clinical practice.

RESULTS

We included 30 studies in the scoping review. The most frequently studied type of eHealth was a telecommunication/telemonitoring system. The survey was completed by 102 participants. The most frequently used types of eHealth were electronic health records, social alarms, and online client portals. A health app was the most frequently preferred type of eHealth. We identified 22 factors that influence the use of eHealth in home care according to health care professionals and home care clients. Influencing factors were organized into the components of the COM-B model, namely capability (n=6), opportunity (n=10), and motivation (n=6). We found that there is no single influencing factor that is key to the complexity of eHealth implementation.

CONCLUSIONS

Different types of eHealth are used, and many types of eHealth are preferred by health care professionals. The identified factors that influence the use of eHealth in home care relate to all components of the COM-B model. These factors need to be addressed and embedded in implementation strategies of eHealth to optimize the use of eHealth in home care.

On the Role of Beliefs and Trust for the Intention to Use Service Robots: An Integrated Trustworthiness Beliefs Model for Robot Acceptance

With the increasing abilities of robots, the prediction of user decisions needs to go beyond the usability perspective, for example, by integrating distinctive beliefs and trust. In an online study (N = 400), first, the relationship between general trust in service robots and trust in a specific robot was investigated, supporting the role of general trust as a starting point for trust formation. On this basis, it was explored—both for general acceptance of service robots and acceptance of a specific robot—if technology acceptance models can be meaningfully complemented by specific beliefs from the theory of planned behavior (TPB) and trust literature to enhance understanding of robot adoption. First, models integrating all belief groups were fitted, providing essential variance predictions at both levels (general and specific) and a mediation of beliefs via trust to the intention to use. The omission of the performance expectancy and reliability belief was compensated for by more distinctive beliefs. In the final model (TB-RAM), effort expectancy and competence predicted trust at the general level. For a specific robot, competence and social influence predicted trust. Moreover, the effect of social influence on trust was moderated by the robot's application area (public > private), supporting situation-specific belief relevance in robot adoption. Taken together, in line with the TPB, these findings support a mediation cascade from beliefs via trust to the intention to use. Furthermore, an incorporation of distinctive instead of broad beliefs is promising for increasing the explanatory and practical value of acceptance modeling.

Intelligent Physical Robots in Health Care: Systematic Literature Review

BACKGROUND

Intelligent physical robots based on artificial intelligence have been argued to bring about dramatic changes in health care services. Previous research has examined the use of intelligent physical robots in the health care context from different perspectives; however, an overview of the antecedents and consequences of intelligent physical robot use in health care is lacking in the literature.

OBJECTIVE

In this paper, we aimed to provide an overview of the antecedents and consequences of intelligent physical robot use in health care and to propose potential agendas for future research through a systematic literature review.

METHODS

We conducted a systematic literature review on intelligent physical robots in the health care field following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Literature searches were conducted in 5 databases (PubMed, Scopus, PsycINFO, Embase, and CINAHL) in May 2021, focusing on studies using intelligent physical robots for health care purposes. Subsequently, the quality of the included studies was assessed using the Mixed Methods Appraisal Tool. We performed an exploratory content analysis and synthesized the findings extracted from the included articles.

RESULTS

A total of 94 research articles were included in the review. Intelligent physical robots, including mechanoid, humanoid, android, and animalistic robots, have been used in hospitals, nursing homes, mental health care centers, laboratories, and patients' homes by both end customers and health care professionals. The antecedents for intelligent physical robot use are categorized into individual-, organization-, and robot-related factors. Intelligent physical robot use in the health care context leads to both non-health-related consequences (emotional outcomes, attitude and evaluation outcomes, and behavioral outcomes) and consequences for (physical, mental, and social) health promotion for individual users. Accordingly, an integrative framework was proposed to obtain an overview of the antecedents and consequences of intelligent physical robot use in the health care context.

CONCLUSIONS

This study contributes to the literature by summarizing current knowledge in the field of intelligent physical robot use in health care, by identifying the antecedents and the consequences of intelligent physical robot use, and by proposing potential future research agendas in the specific area based on the research findings in the literature and the identified knowledge gaps.

Are nursing home employees ready for the technical evolution? German-wide survey on the status quo of affinity for technology and technology interaction

Background

Technological devices can support nursing home employees; however, their perspective is not sufficiently studied. Our aims were thus to (a) examine affinity for technology and technology interaction and related sociodemographic confounders, as well as (b) detect possible requirements and boundary conditions relevant for the development and implementation of assistive technologies among nursing home employees.

Methods

We conducted an online survey between May and July of 2022 among 200 nursing home employees in Germany. The survey included two questionnaires, that is, Affinity for Technology Interaction (ATI) and Affinity for Technology-Electronic Devices (TA-EG; subscales TA-EG-Enthusiasm, TA-EG-Competence, TA-EG-Positive Consequences, and TA-EG-Negative Consequences), as well as sociodemographic variables, that is, age, gender, professional groups, education/graduation level. We carried out factorial variance and multiple regression analyses.

Results

There were differences between age groups in ATI (lower score with increasing age) and between gender, age, and professional group in TA-EG (lower score for females, participants with higher ages, and nursing home managers). Predictors of ATI were age and professional group, predictors of TA-EG, TA-EG-Enthusiasm, and TA-EG-Competence were gender, age, and professional group. Predictors of TA-EG-Positive Consequences were education and professional group.

Conclusions

We observed rather high affinity for technology and technology interaction values overall, and particularly for nursing home employees compared to managers. Significant predictors for technology affinity and interaction may have important implications, for example the perspectives of nursing home employees and managers should be considered separately in the technological design, development, and implementation process. Furthermore, an open dialogue between all stakeholders should be encouraged to increase the probability of actual technology use.

Medical artificial intelligence ethics: A systematic review of empirical studies

Background

Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and regulatory implications of medical AI, and empirical research on stakeholders' knowledge, attitude, and practices has started to emerge. This study is a systematic review of published empirical studies of medical AI ethics with the goal of mapping the main approaches, findings, and limitations of scholarship to inform future practice considerations.

Methods

We searched seven databases for published peer-reviewed empirical studies on medical AI ethics and evaluated them in terms of types of technologies studied, geographic locations, stakeholders involved, research methods used, ethical principles studied, and major findings.

Findings

Thirty-six studies were included (published 2013-2022). They typically belonged to one of the three topics: exploratory studies of stakeholder knowledge and attitude toward medical AI, theory-building studies testing hypotheses regarding factors contributing to stakeholders' acceptance of medical AI, and studies identifying and correcting bias in medical AI.

Interpretation

There is a disconnect between high-level ethical principles and guidelines developed by ethicists and empirical research on the topic and a need to embed ethicists in tandem with AI developers, clinicians, patients, and scholars of innovation and technology adoption in studying medical AI ethics.

Technology-assisted assessment of spasticity: a systematic review

BACKGROUND

Spasticity is defined as "a motor disorder characterised by a velocity dependent increase in tonic stretch reflexes (muscle tone) with exaggerated tendon jerks". It is a highly prevalent condition following stroke and other neurological conditions. Clinical assessment of spasticity relies predominantly on manual, non-instrumented, clinical scales. Technology based solutions have been developed in the last decades to offer more specific, sensitive and accurate alternatives but no consensus exists on these different approaches.

METHOD

A systematic review of literature of technology-based methods aiming at the assessment of spasticity was performed. The approaches taken in the studies were classified based on the method used as well as their outcome measures. The psychometric properties and usability of the methods and outcome measures reported were evaluated.

RESULTS

124 studies were included in the analysis. 78 different outcome measures were identified, among which seven were used in more than 10 different studies each. The different methods rely on a wide range of different equipment (from robotic systems to simple goniometers) affecting their cost and usability. Studies equivalently applied to the lower and upper limbs (48% and 52%, respectively). A majority of studies applied to a stroke population (N = 79). More than half the papers did not report thoroughly the psychometric properties of the measures. Analysis identified that only 54 studies used measures specific to spasticity. Repeatability and discriminant validity were found to be of good quality in respectively 25 and 42 studies but were most often not evaluated (N = 95 and N = 78). Clinical validity was commonly assessed only against clinical scales (N = 33). Sensitivity of the measure was assessed in only three studies.

CONCLUSION

The development of a large diversity of assessment approaches appears to be done at the expense of their careful evaluation. Still, among the well validated approaches, the ones based on manual stretching and measuring a muscle activity reaction and the ones leveraging controlled stretches while isolating the stretch-reflex torque component appear as the two promising practical alternatives to clinical scales. These methods should be further evaluated, including on their sensitivity, to fully inform on their potential.

Integrating drones in response to public health emergencies: A combined framework to explore technology acceptance

The aim of the study was to propose and test an integrated model combining the technology acceptance model (TAM), task-technology fit (TTF), social motivation, and drone-related perceived risks to explore the intention to use drones in public health emergencies (PHEs). We conducted a survey among the Israeli population, yielding a sample of 568 participants. Structural equation modeling was implemented to test the research hypotheses. The results showed that our integrated model provided a robust and comprehensive framework to perform an in-depth investigation of the factors and mechanisms affecting drone acceptance in PHEs. First, ease of use, attitudes, individual-technology fit, task-technology fit, and social influence significantly and directly influenced users' behavioral intention to utilize drone technology. Second, attitudes were significant mediators of the effects of social influence and perceived risks on the intention to use drones. Finally, significant relationships between TAM, TTF, social motivation, and perceived risks were also observed. Theoretical aspects and practical implications-which can serve as the basis for shaping a positive development in drone public acceptance in PHEs and in general-are discussed.

The trustworthy and acceptable HRI checklist (TA-HRI): questions and design recommendations to support a trust-worthy and acceptable design of human-robot interaction

This contribution to the journal Gruppe. Interaktion. Organisation. (GIO) presents a checklist of questions and design recommendations for designing acceptable and trustworthy human-robot interaction (HRI). In order to extend the application scope of robots towards more complex contexts in the public domain and in private households, robots have to fulfill requirements regarding social interaction between humans and robots in addition to safety and efficiency. In particular, this results in recommendations for the design of the appearance, behavior, and interaction strategies of robots that can contribute to acceptance and appropriate trust. The presented checklist was derived from existing guidelines of associated fields of application, the current state of research on HRI, and the results of the BMBF-funded project RobotKoop. The trustworthy and acceptable HRI checklist (TA-HRI) contains 60 design topics with questions and design recommendations for the development and design of acceptable and trustworthy robots. The TA-HRI Checklist provides a basis for discussion of the design of service robots for use in public and private environments and will be continuously refined based on feedback from the community.

Nurse preferences of caring robots: A conjoint experiment to explore most valued robot features

AIM

Due to the COVID pandemic and technological innovation, robots gain increasing role in nursing services. While studies investigated negative attitudes of nurses towards robots, we lack an understanding of nurses' preferences about robot characteristics. Our aim was to explore how key robot features compare when weighed together.

METHODS

Cross-sectional research design based on a conjoint analysis approach. Robot dimensions tested were: (1) communication; (2) look; (3) safety; (4) self-learning ability; and (5) interactive behaviour. Participants were asked to rank robot profile cards from most to least preferred.

RESULTS

In order of importance, robot's ability to learn ranked first followed by behaviour, look, operating safety and communication. Most preferred robot combination was 'robot responds to commands only, looks like a machine, never misses target, runs programme only and behaves friendly'.

CONCLUSIONS

Robot self-learning capacity was least favoured by nurses showing potential fear of robots taking over core nurse competencies.

Exploring home health-care robots adoption in Malaysia: extending the UTAUT model

Purpose

This study aims to examine the possible factors affecting Malaysians’ intention to adopt new technology such as home health-care robots (HHRs) based on the extended unified theory of acceptance and use of technology (UTAUT) model. The variable price, awareness and alternative attractiveness were added since HHRs is a new technology in Malaysia.

Design/methodology/approach

This study collected the self-administered questionnaire from 316 respondents who are currently taking care of elderly or disabled people at home. The UTAUT variables, price and awareness were the predictors, and the intention to adopt HHRs was the independent variable, with alternative attractiveness as the moderator. The partial least squares structural equation modeling was used for analysis of the measurement model and the structural model of this study.

Findings

The results show that performance expectancy, social influence, facilitating conditions, price and awareness significantly and positively affect Malaysians’ intention to adopt HHRs. Alternative attractiveness moderates the relationship between price and intention to adopt HHRs.

Research limitations/implications

The findings provide insights to marketers, managers and policymakers in identifying the right strategies to promote HHRs and thus, solving the problem of scarcity in caretakers for elderly and disabled people.

Originality/value

This study adds value to the current literature by integrating price and awareness constructs with the UTAUT model. This study also examines the moderating effect of alternative attractiveness on the intention to adopt HHRs, which is still limited but significant for developing nations.

Healthcare Workers' Point of View on Medical Robotics During COVID-19 Pandemic - A Scoping Review

COVID-19 affected how healthcare workers interact with patients. Medical technology and robotics are developed in hospital settings to limit human contact. The aim of this review is to elucidate what kind of medical robotics is required for healthcare workers during COVID-19 pandemic. This review was obtained from electronic databases such as Google Scholar, PubMed, EBSCO, and Cochrane reviews were searched for articles using keywords such as "healthcare professional" OR "health worker" AND "COVID-19" AND "robot application" OR "robotics" OR "health technology" AND "needs assessment" OR "expectation" OR "perception" published during 2020 to 2021. Inclusion criteria were full-text articles related to assessment of healthcare workers' need for medical robotics during COVID-19 pandemics. Exclusion criteria included abstracts, duplicate articles, blogs, news articles, promotional brochures, and conference proceedings. A total of 13,692 articles were identified through the search engines (PubMed 179, Cochrane Library 1300, EBSCO 13, Google Scholar 12,200). Five full-text articles fulfilled the inclusion criteria. Determining robotic functions is important to healthcare workers who will be user of such medical technology. This review divided robotic functions into medical, operational, movement, and social functions. Healthcare workers' demands for robotics were also influenced by the types of robots, such as examination robots, robot-based sample test and medicine production, surgery and rehabilitation robots, disinfection and cleaning robots, delivery and logistic robot, telemedicine, and telepresence robots. Medical robotics is required for healthcare workers during the COVID-19 pandemic. The highest demands for medical robotics functions include cardiac measurements and oxygen saturation monitoring (medical functions); examination record delivery, video and image play, and medical information delivery (operational functions); and the ability to recognize and avoid obstacles (movement functions). Disinfection and cleaning robots were the type of robots with the highest demand among healthcare workers.

Novelty Experience in Prolonged Interaction: A Qualitative Study of Socially-Isolated College Students' In-Home Use of a Robot Companion Animal

Social distancing policies such as limits on public gatherings and contact with others were utilized around the world to slow the spread of COVID-19. Yet, decreased social interactions may also threaten people's well-being. In this project, we sought to understand novelty-relevant experiences surrounding in-home companion robot pets for adults that were living in some degree of social isolation due to the COVID-19 pandemic. After 6-weeks of participants living with the robot companion, we conducted semi-structured interviews (N = 9) and six themes emerged from our iterative analysis (expectations versus reality, ontological comparisons, interactions, third-party influence, identity, and comfort). Findings suggest that novelty is a complex phenomenon consisting of various elements (i.e., imagined novelty, technology novelty, and relational novelty). Each component influences the user's experience. Our findings also suggest that our understanding of novelty as a nonlinear resource may hold important implications for how we view human-robot relationships beyond initial encounters.

Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study

Background

In recent years, the use of mobile health (mHealth) apps to manage chronic diseases has increased significantly. Although mHealth apps have many benefits, their acceptance is still low in certain areas and groups. Most mHealth acceptance studies are based on technology acceptance models. In particular, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was developed to predict technology acceptance in a consumer context. However, to date, only a few studies have used the UTAUT2 model to predict mHealth acceptance and confirm its suitability for the health sector. Thus, it is unclear whether the UTAUT2 model is suitable for predicting mHealth acceptance and whether essential variables for a health-related context are missing.

Objective

This study aims to validate the suitability of UTAUT2 for predicting mHealth acceptance.

Methods

In this study, diabetes was used as an example as mHealth apps are a significant element of diabetes self-management. In addition, diabetes is one of the most common chronic diseases affecting young and older people worldwide. An explorative literature review and guided interviews with 11 mHealth or technology acceptance experts and 8 mHealth users in Austria and Germany were triangulated to identify all relevant constructs for predicting mHealth acceptance. The interview participants were recruited by purposive sampling until theoretical saturation was reached. Data were analyzed using structured content analysis based on inductive and deductive approaches.

Results

This study was able to confirm the relevance of all exogenous UTAUT2 constructs. However, it revealed two additional constructs that may also need to be considered to better predict mHealth acceptance: trust and perceived disease threat.

Conclusions

This study showed that the UTAUT2 model is suitable for predicting mHealth acceptance. However, the model should be extended to include 2 additional constructs for use in the mHealth context.

Do People Trust in Robot-Assisted Surgery? Evidence from Europe

(1) Background: The goal of the paper was to establish the factors that influence how people feel about having a medical operation performed on them by a robot. (2) Methods: Data were obtained from a 2017 Flash Eurobarometer (number 460) of the European Commission with 27,901 citizens aged 15 years and over in the 28 countries of the European Union. Logistic regression (odds ratios, OR) to model the predictors of trust in robot-assisted surgery was calculated through motivational factors, using experience and sociodemographic independent variables. (3) Results: The results obtained indicate that, as the experience of using robots increases, the predictive coefficients related to information, attitude, and perception of robots become more negative. Furthermore, sociodemographic variables played an important predictive role. The effect of experience on trust in robots for surgical interventions was greater among men, people between 40 and 54 years old, and those with higher educational levels. (4) Conclusions: The results show that trust in robots goes beyond rational decision-making, since the final decision about whether it should be a robot that performs a complex procedure like a surgical intervention depends almost exclusively on the patient’s wishes.

Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses

OBJECTIVE

The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area.

BACKGROUND

Human-robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs.

METHOD

The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis.

RESULTS

Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI.

CONCLUSION

The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered.

APPLICATION

The identified trust factors can be used in order to promote appropriate levels of trust in robots.

Attitudes toward the use of humanoid robots in healthcare—a cross-sectional study

The use of robotic technology in healthcare is increasing. The aim was to explore attitudes toward the use of humanoid robots in healthcare among patients, relatives, care professionals, school actors and other relevant actors in healthcare and to analyze the associations between participants’ background variables and attitudes. The data were collected through a cross-sectional survey (N = 264) in 2018 where participants met a humanoid robot. The survey was comprised of background variables and items from a modified Robot Attitude Scale. Multiple linear regression analysis and Spearman’s Rho correlation were used to analyze associations between variables. Most of the participants were positive toward the use of humanoid robots in healthcare and only a few were negative. Attitudes toward the use of humanoid robots were more positive among other relevant actors, such as service personnel and politicians in healthcare, participants with a higher educational level and older adults. More research is needed on the reasons underlying negative attitudes because these might affect the introduction of humanoid robots in healthcare. A careful evaluation of appropriate first target groups as well as which tasks are appropriate for humanoid robots to perform in healthcare are needed.

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

BACKGROUND

Considering the increasing demand for health services by older people and the ongoing COVID-19 pandemic, digital health is commonly viewed to offer a pathway to provide safe and affordable health services for older adults, thus enabling self-management of their health while health care systems are struggling. However, several factors cause older people to be particularly reluctant to adopt digital health technologies such as mobile health (mHealth) tools. In addition to previously studied technology acceptance factors, those related to perceived risks of mHealth use (eg, leakage of sensitive information or receiving incorrect health recommendations) may further diminish mHealth adoption by older adults.

OBJECTIVE

The aim of this study was to explore the relationship between perceived risks of using mHealth applications and the intention to use these applications among older adults.

METHODS

We designed a cross-sectional study wherein a questionnaire was used to collect data from participants aged 65 years and older in the Netherlands. Perceived risk was divided into four constructs: privacy risk, performance risk, legal concern, and trust. Linear regression analyses were performed to determine the associations between these perceived risk constructs and the intention to use mHealth applications.

RESULTS

Linear regression per perceived risk factor showed that each of the four constructs is significantly associated with the intention to use mobile medical applications among older adults (adjusted for age, sex, education, and health status). Performance risk (β=-.266; P=<.001), legal concern (β=-.125; P=.007), and privacy risk (β=-.100; P=.03) were found to be negatively correlated to intention to use mHealth applications, whereas trust (β=.352; P=<.001) was found to be positively correlated to the intention to use mHealth applications.

CONCLUSIONS

Performance risk, legal concern, and privacy risk as perceived by older adults may substantially and significantly decrease their intention to use mHealth applications. Trust may significantly and positively affect this intention. Health care professionals, designers of mHealth applications, and policy makers can use these findings to diminish performance risks, and tailor campaigns and applications to address legal and privacy concerns and promote mHealth uptake and health care access for older adults, especially during the COVID-19 pandemic.

The introduction of care robots as a leadership challenge in home care facilities in Finland

Aims

This paper analyses the factors that influence home care employees’ intention to introduce robots.

Background

The introduction of different kinds of care robots is a topical issue in elderly care now and in the near future.

Methods

Cross‐sectional research conducted through a questionnaire. The survey data (N = 162) were collected in five locations around Finland in 2019. The analysis was carried out by regression analysis, Sobel test and by Hayes’ bootstrapping method.

Results

The results show that self‐efficacy is pivotal in the willingness to introduce care robots. Employees’ age increases the enthusiasm to introduce robots but reduces self‐efficacy. Work engagement does not correlate with self‐efficacy or behavioural intention related to the introduction of care robots.

Conclusions

The present paper reveals the significance of attitudes, cognitive factors and age in the adoption of care robots in home care facilities.

Practical implications

It is important to pay attention to supporting the employees’ sense of technology management and the construction of a robot‐positive atmosphere when introducing care robots, and the development of skills of older employees and employees with a lower educational level should be supported.

Expectations and Perceptions of Healthcare Professionals for Robot Deployment in Hospital Environments During the COVID-19 Pandemic

Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.

Do Privacy Concerns About Social Robots Affect Use Intentions? Evidence From an Experimental Vignette Study

While the privacy implications of social robots have been increasingly discussed and privacy-sensitive robotics is becoming a research field within human–robot interaction, little empirical research has investigated privacy concerns about robots and the effect they have on behavioral intentions. To address this gap, we present the results of an experimental vignette study that includes antecedents from the privacy, robotics, technology adoption, and trust literature. Using linear regression analysis, with the privacy-invasiveness of a fictional but realistic robot as the key manipulation, we show that privacy concerns affect use intention significantly and negatively. Compared with earlier work done through a survey, where we found a robot privacy paradox, the experimental vignette approach allows for a more realistic and tangible assessment of respondents' concerns and behavioral intentions, showing how potential robot users take into account privacy as consideration for future behavior. We contextualize our findings within broader debates on privacy and data protection with smart technologies.

Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers

INTRODUCTION

Mobile health (mHealth) applications gain popularity due to the increasing number of mobile phone usage and internet penetration, which might help some of Indonesia's medical issues. However, the uptake of mHealth applications is still low in Indonesia. This study is aimed at understanding the factors that drive individuals to adopt mHealth applications and their impact on the intention to recommend.

METHODS

We applied a new model that combines three different theories with some other constructs: an extended unified theory of acceptance and use of technology, diffusion of innovation, and the internet customer trust model. The study used a cross-sectional study design with partial least squares causal modeling approach.

RESULTS

There are 787 respondents in our study, with the majority of them being female, young adults. Our model could explain 53.2% of the variance of intention to adopt while explaining 48.3% of the variance of intention to recommend. Initial trust in mHealth platform (β = 0.373, p = <0.001), facilitating conditions (β = 0.131, p = <0.01), and performance expectancy (β = 0.099, p = <0.05) are the top three most important drivers of intention to adopt mHealth applications. Lastly, importance-performance map analysis (IPMA) showed that the mHealth application's initial trust is the most important construct with a high-performance score. Discussion. Mobile health developers and managers need to improve initial trust in the mHealth platform, facilitating conditions, and performance expectancy when developing the applications. With a medium Q 2 predict, these factors can be applied out of the research context with medium predictive power.

Routine Health Information Systems in the European Context: A Systematic Review of Systematic Reviews

(1) Background: The aim of this study is to provide a better understanding of the requirements to improve routine health information systems (RHISs) for the management of health systems, including the identification of best practices, opportunities, and challenges in the 53 countries and territories of the WHO European region. (2) Methods: We conducted an overview of systematics reviews and searched the literature in the databases MEDLINE/PubMed, Cochrane, EMBASE, and Web of Science electronic databases. After a meticulous screening, we identified 20 that met the inclusion criteria, and RHIS evaluation results were presented according to the Performance of Routine Information System Management (PRISM) framework. (3) Results: The reviews were published between 2007 and 2020, focusing on the use of different systems or technologies and aimed to analyze interventions on professionals, centers, or patients' outcomes. All reviews examined showed variability in results in accordance with the variability of interventions and target populations. We have found different areas for improvement for RHISs according to the three determinants of the PRISM framework that influence the configuration of RHISs: technical, organizational, or behavioral elements. (4) Conclusions: RHIS interventions in the European region are promising. However, new global and international strategies and the development of tools and mechanisms should be promoted to highly integrate platforms among European countries.

Patients' perceptions of teleconsultation during COVID-19: A cross-national study

In recent months, humanity has had to deal with a worldwide pandemic called COVID-19, which has caused the death of hundreds of thousands of people and paralyzed the global economy. Struggling to cure infected patients while continuing to care for patients with other pathologies, health authorities have faced the lack of medical staff and infrastructure. This study aimed to investigate the acceptance of teleconsultation solutions by patients, which help to avoid the spread of the disease during this pandemic period. The model was built using some constructs of the technology acceptance model UTAUT2, Personal traits, Availability, and Perceived Risks. A new scale on Contamination Avoidance was developed by the authors. The questionnaire was disseminated in several countries in Europe and Asia and a total sample of 386 respondents was collected. The results emphasize the huge impact of Performance Expectancy, the negative influence of Perceived Risk, and the positive influence of Contamination Avoidance on the adoption of teleconsultation solutions. The findings highlight the moderating effects of Age, Gender, and Country.

Robotics in Simulated COVID-19 Patient Room for Health Care Worker Effector Tasks: Preliminary, Feasibility Experiments

The coronavirus disease 2019 (COVID-19) pandemic has strained health care systems and personal protective equipment (PPE) supplies globally. We hypothesized that a collaborative robot system could perform health care worker effector tasks inside a simulated intensive care unit (ICU) patient room, which could theoretically reduce both PPE use and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposures. We planned a prospective proof-of-concept feasibility and design pilot study to test 5 discrete medical tasks in a simulated ICU room of a COVID-19 patient using a collaborative robot: push a button on intravenous pole machine when alert occurs for downstream occlusion, adjust ventilator knob, push button on ICU monitor to silence false alerts, increase oxygen flow on wall-mounted flow meter to allow the patient to walk to the bathroom and back (dial-up and dial-down oxygen flow), and push wall-mounted nurse call button. Feasibility was defined as task completion robotically. A training period of 45 minutes to 1 hour was needed to program the system de novo for each task. In less than 30 days, the team completed 5 simple effector task experiments robotically. Selected collaborative robotic effector tasks appear feasible in a simulated ICU room of the COVID-19 patient. Theoretically, this robotic approach could reduce PPE use and staff SARS-CoV-2 exposure. It requires future validation and health care worker learning similar to other ICU device training.

Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

Background

Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey’s most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users.

Objective

The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users.

Methods

We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling.

Results

Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app.

Conclusions

This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform’s dietitians to ensure that users’ interactions with the dietitians are based on trust for the platform and the brand.

Education and Training on Electronic Medical Records (EMRs) for health care professionals and students: A Scoping Review

BACKGROUND AND OBJECTIVES

The ability of health care providers and students to use EMRs efficiently can lead to achieving improved clinical outcomes. Training policies and strategies play a major role in successful technology implementation and ongoing use of the EMR systems. To provide evidence-based guidance for developing and implementing educational interventions and training, we reviewed and summarized the current literature on EMR training targeting both healthcare professionals (HCP) and students.

METHODS

We used the Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA extension of scoping reviews (PRISMA-ScR) checklist for reporting our review. 46 full-text articles that met the eligibility criteria were selected for the review. Narrative synthesis was performed to summarize the evidence using numerical and descriptive analysis. We used inductive content analysis for categorizing the training methods. Also, the modified version of the Kirkpatrick's levels model was used for abstracting the training outcome.

RESULTS

Five types of training methods were identified: one-on-one training, peer-coach training, classroom training (CRT), computer-based training (CBT), and blended training. A variety of CBT platforms were used, including a prototype academic electronic medical record system (AEMR), AEMR/simulated EMR (Sim-EMR), mobile based AEMR, eLearning, and electronic educational materials. Each training intervention could have resulted in several outcomes. Most outcomes were related to levels 1-3 of the Kirkpatrick model that involves learners (n = 108), followed by level 4a that involves organizations (n = 7), and lastly level 4b that involves patients (n = 1). The outcomes related to participants' knowledge (level 2b) was the most often measured training outcome (n = 44).

CONCLUSIONS

This review presents a comprehensive synthesis of the evidence on EMR training. A variety of training methods, participants, locations, strategies, and outcomes were described in the studies. Training should be aligned with the particular training needs, training objectives, EMR system utilized, and organizational environment. A training plan should include an overall goal and SMART (Specific, Measurable, Achievable, Realistic, Tangible) training objectives, that would allow a more rigorous evaluation of the training outcomes.

Impacts of a Care Robotics Project on Finnish Home Care Workers' Attitudes towards Robots

Technological advances in elderly care have been rapid, and the introduction of robots in care will be a topical issue in the near future. There has been little research into the possibility of influencing care workers’ attitudes towards robots by project activities, and how to make the change easier for work communities. This study focuses on a robotics project that took place in elderly and home care services in one municipality in Finland (total of 45 care workers). During the project, four robotics workshops and one extended pilot session were implemented. The study follows quasi-experimental settings, and it included two measurements (before and after project activities) and a control group, but no randomization. The data were collected by questionnaires and were analyzed statistically. The project under study brought about minor positive changes in home care workers’ attitudes towards the usefulness of care robots. In the final measurement, the difference between the test group and the control group was significant in the two dimensions of positive attitudes. The research supports the hypothesis that project activities can be used to influence home care workers’ attitudes towards robots. This can also facilitate the introduction of care robots in home care services. However, the construction of a technology-positive care culture is a long-term process, which requires training and development, technological development and strong strategic management at various levels.