Social Acceptance of Mobile Health among Young Adults in Japan: An Extension of the UTAUT Model

Understanding the Role of Technology Anxiety in the Adoption of Digital Health Technologies (DHTs) by Older Adults with Chronic Diseases in Shanghai: An Extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model

The unprecedented rapid growth of digital health has brought new opportunities to the health field. However, elderly patients with chronic diseases, as an important potential beneficiary group, are affected by the digital divide, leading to unsatisfactory usage of digital health technologies (DHTs). Our study focused on the factors influencing the adoption of DHTs among this vulnerable group. To extend the UTAUT theory, technology anxiety and several demographic predictors were included to address the age characteristics of the respondents. An on-site survey was conducted in general, district, and community hospitals in Shanghai (n = 309). Facilitating conditions negatively influenced technology anxiety. Technology anxiety hindered behavioural intention. Social influence had a significant but negative impact on behavioural intention. Education, whether older adults have had experience with DHTs and previous smartphone usage experiences were significantly associated with technology anxiety. The findings provide valuable information for multiple stakeholders, including family members of elderly users, product designers, and policymakers. Ameliorating facilitating conditions, improving devices' usage experience, encouraging attempts and focusing on groups with lower educational levels can help to reduce technology anxiety and promote DHT acceptance and use in older age groups.

Development and Validation of the Saudi Telehealth Acceptance Scale Based on the Unified Theory of Acceptance and Use of Technology

Background

Telehealth is in early stages in the Kingdom of Saudi Arabia (KSA). Identifying barriers to using telehealth is an essential step prior to its implementation; however, no theoretically based scales exist for the same purpose.

Objective

To design a theory-based scale capable of measuring telehealth users' perceptions of benefits, barriers, and social influence in the KSA.

Methods

The Saudi Telehealth Acceptance Scale (STAS) was constructed based on the Unified Theory of Acceptance and Use of Technology and its content validity was determined by eight experts using the modified Delphi process. Only items with a content validity index score of ≥0.83 were chosen. Finally, the face validity of the questionnaire for healthcare professionals, which was in English, was determined by eight healthcare professionals, and for the patient version, which was in Arabic, by six patients.

Results

Initially, 37 items were identified; however, after Round 1, 29 items remained and after Round 2, 24 items were included in the STAS for both healthcare professionals and patients. In the healthcare professionals scale, there were 7 performance expectancy (PE) items, 6 effort expectancy (EE) items, 3 social influence (SI) items, 4 facilitating conditions (FC) items, and 4 behavioral intention (BI) items, while in the scale for patients, there were 9 PE items, 4 EE items, 3 SI items, 4 FC items, and 4 BI items.

Conclusions

The two new scales showed evidence of content and face validity. The significance of the two scales is that they were both designed in the context of the healthcare system in the KSA and could provide standardized data collection tools to measure the acceptance of telehealth among HCPs and patients.

Scientific epistemology beliefs and acceptance of Traditional Chinese Medicine: A multigroup analysis based on the UTAUT model in Southern China

Purpose

This study for the first time delves into the intricate relationship between scientific literacy and the acceptance of traditional Chinese medicine (TCM) by employing a multigroup path analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. We adopted Scientific Epistemology Belief (SEB) as an indicator for measuring scientific literacy due to its comprehensive reflection of individuals' understanding of scientific knowledge and knowing. In assessing TCM acceptance, we focused on Chinese parents' receptivity towards pediatric TCM, as it offers a more genuine representation of actual inclinations.

Methods

A convenience sample of 1016 Chinese parents in Southern China was assessed using online Likert-scale questionnaires on SEB and UTAUT determinants (including performance expectancy, social influence, risk awareness, and facilitating conditions). A K-means cluster analysis was employed to discern distinct SEB profiles, followed by a multigroup path analysis to ascertain UTAUT model variations across these profiles.

Results

Five SEB profiles were identified, namely, intermediate, absolutistic, multiplistic, sophisticated, and evidence-based. Evidence-based believers manifested the highest pediatric TCM acceptance, albeit with elements of blind faith, while multiplistic skeptics, prone to questioning everything, displayed the least acceptance. The absolutistic, intermediate, and sophisticated demonstrated moderate TCM acceptance levels, with the intermediate profile outscoring both absolutistic and sophisticated. These findings highlight that individuals with high scientific literacy do not blindly endorse TCM, nor do those with limited scientific understanding fully appreciate TCM's merits.

Conclusion

SEB significantly moderates TCM acceptance factors in the UTAUT model, indicating that extremes in scientific knowledge spectrum result in less balanced TCM perspectives. Our findings pave the way for novel insights into harmonizing modern and traditional medical practices.

How Social Influence Promotes the Adoption of Mobile Health among Young Adults in China: A Systematic Analysis of Trust, Health Consciousness, and User Experience

As mobile health (mHealth) offers several advantages in healthcare, researchers are exploring the motivational factors for its adoption. However, few studies have elucidated the complex relationship between social influence and behavioral intentions to adopt mHealth among young adults in China. This study explored the impact of social influence on young adults' behavioral intentions to adopt mHealth, the mediating roles of trust and health consciousness, and the moderating effect of mHealth user experience on the relationship between the predictors. In total, 300 valid responses were collected from a university in China, and a research model was developed. The partial least squares structural equation modeling method was used to verify the relationship between the main research variables. mHealth adoption behavioral intentions among young adults were significantly positively impacted by social influence; it indirectly increased mHealth adoption behavioral intentions by positively affecting trust and health consciousness. mHealth use weakened the positive impact of social influence on trust and health consciousness, while user experience positively moderated the relationship between health consciousness and behavioral intentions. Trust and health consciousness play important roles in the complex multivariate relationships between social influence and behavioral intentions to adopt mHealth. Future research should consider the moderating role of the mHealth user experience. These findings enrich the mHealth technology acceptance theory framework and provide specific guidance strategies for marketing mHealth applications.

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.

Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology

Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers' intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and collected quantitative data from conveniently selected respondents through an online survey, which involved 2068 Telegram users in Indonesia. All data were subjected to the analysis of partial least square- structural equation modeling (PLS-SEM). The obtained results demonstrated the moderating effect of perceived product value on the relationship between intention to use m-Health application (m-health app) and actual usage of m-Health app and the mediating effects of intention to use m-Health app on the relationships of perceived critical mass, perceived usefulness, perceived convenience, perceived technology accuracy, and perceived privacy protection on actual usage of m-Health app. However, the intention to use m-Health app did not mediate the influence of health consciousness and health motivation on the actual usage of m-Health app. Overall, this study's findings on the significance of intention to use m-Health app and perceived product value based on the UTAUT framework serve as insightful guideline to expand the usage of m-Health app among consumers.

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.

Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (β = 0.504, CR = 5.064, p < 0.05) and self-concept (β = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.

Investigating Factors Influencing Medical Practitioners' Resistance to and Adoption of Internet Hospitals in China: Mixed Methods Study

BACKGROUND

The swift shift toward internet hospitals has relied on the willingness of medical practitioners to embrace new systems and workflows. Low engagement or acceptance by medical practitioners leads to difficulties in patient access. However, few investigations have focused on barriers and facilitators of adoption of internet hospitals from the perspective of medical practitioners.

OBJECTIVE

This study aims to identify both enabling and inhibiting predictors associated with resistance and behavioral intentions of medical practitioners to use internet hospitals by combining the conservation of resources theory with the Unified Theory of Acceptance and Use of Technology and technostress framework.

METHODS

A mixed methods research design was conducted to qualitatively identify the factors that enable and inhibit resistance and behavioral intention to use internet hospitals, followed by a quantitative survey-based study that empirically tested the effects of the identified factors. The qualitative phase involved conducting in-depth interviews with 16 experts in China from June to August 2022. Thematic analysis was performed using the qualitative data analysis software NVivo version 10 (QSR International). On the basis of the findings and conceptual framework gained from the qualitative interviews, a cross-sectional, anonymous, web-based survey of 593 medical practitioners in 28 provincial administrative regions of China was conducted. The data collected were analyzed using the partial least squares method, with the assistance of SPSS 27.0 (IBM Corp) and Mplus 7.0 (Muthen and Muthen), to measure and validate the proposed model.

RESULTS

On the basis of qualitative results, this study identified 4 facilitators and inhibitors, namely performance expectancy, social influence, work overload, and role ambiguity. Of the 593 medical practitioners surveyed in the quantitative research, most were female (n=364, 61.4%), had a middle title (n=211, 35.6%) or primary title (n=212, 35.8%), and had an average use experience of 6 months every year. By conducting structural equation modeling, we found that performance expectancy (β=-.55; P<.001) and work overload (β=.16; P=.005) had the most significant impact on resistance to change. Resistance to change fully mediated the influence of performance expectancy and partially mediated the influences of social influence (variance accounted for [VAF]=43.3%; P=.002), work overload (VAF=37.2%; P=.03), and role ambiguity (VAF=12.2%; P<.001) on behavioral intentions to use internet hospitals. In addition, this study found that the sex, age, professional title, and use experience of medical practitioners significantly moderated the aforementioned influencing mechanisms.

CONCLUSIONS

This study investigated the factors that facilitate or hinder medical practitioners' resistance to change and their behavioral intentions to use internet hospitals. The findings suggest that policy makers avoid the resistance and further promote the adoption of internet hospitals by ensuring performance expectancy and social influence and eliminating work overload and role ambiguity.

Acceptance of Smart Technologies in Blended Learning: Perspectives of Chinese Medical Students

Smart technologies are essential in improving higher education teaching and learning. The present study explores the factors that influence students' behavioural intentions to adopt and use smart technologies in blended learning. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, a survey of 305 students was conducted to collect data. A structural equation model was applied to analyse the data. The findings show that adopting smart technologies requires appropriate social context and organizational support. Moreover, the data indicated that performance expectancy, effort expectancy, social influence, hedonic motivation, and habit are vital in determining students' behavioural intention to use smart technologies. However, facilitating conditions and price value were found to have no significant impact on the students' behavioural intention to use smart technologies. The study contributes to a better understanding of the nexus of blended learning and smart technologies, thus improving students' experiences in blended learning settings.