e.g. mhealth
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Skip search results from other journals and go to results- 51 Journal of Medical Internet Research
- 20 JMIR Formative Research
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- 13 JMIR mHealth and uHealth
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Researching technology adoption on an individual level is a core focus of IS research [14,15]. Adoption primarily concerns the individual user’s acceptance of technology as well as the adoption decision [15,16]. To explain technology adoption, the most well-known IS theories from an individual perspective are the Technology Adoption Model (TAM) [21] and the Unified Theory of Acceptance and Use of Technology (UTAUT) [14].
J Med Internet Res 2025;27:e63668
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Although many studies examine the functionality, accessibility, and usability of PHRs, as well as their barriers and benefits [5,9,10,12], fewer studies focus on PHR adoption by patients and health care professionals [14], which is essential to ensure PHR usage. Rouleau et al [15] mapped the theories, models, and frameworks in a scoping review that addressed the adoption, implementation, and embedment of e Health.
J Med Internet Res 2025;27:e71915
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It is equally crucial to explore the factors associated with the attitudes and perceptions of medical staff toward the adoption of AI in health care. Previous studies have demonstrated that variations in views on AI can be ascribed to a range of factors, including age, sex, educational level, and experience related to AI [15].
J Med Internet Res 2025;27:e75343
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Given their central role in patient care, nurses are key users of digital nursing technologies (DNTs) and play a crucial role in their adoption and effective implementation in nursing care settings [6].
J Med Internet Res 2025;27:e64616
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In the context of e-learning, implementation outcomes include the acceptability, adoption, appropriateness, feasibility, fidelity, and sustainability of e-learning resources in teaching and learning [8]. When e-learning objects are released as open content, reach and discoverability become important.
To date, several models have been used to evaluate the effectiveness and implementation of e-learning resources.
JMIR Med Educ 2025;11:e63882
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Managerial Challenges in Digital Health: Bibliometric and Network Analysis
Through content analysis, we were able to label cluster 1 as “user adoption and engagement in m Health.” Cluster 1 also examined the adoption of m Health services in low-income countries, specifically India and Bangladesh. Cluster 1 explored emotional bonding with m Health apps, gamification, and cross-country analysis of adoption patterns.
J Med Internet Res 2025;27:e57980
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The study revealed a high level of conformity in perspectives between physicians and patients regarding the adoption and future use of digital health tools and telemedicine. Both groups showed parallel levels of adoption for electronic appointment systems and similar historical use patterns of telemedicine offerings. It yielded a comparable evaluation of the future importance of digital health, with no significant differences observed in their responses.
JMIR Hum Factors 2025;12:e60779
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Our scenario analysis explored the potential adoption of 3 mobile apps using the base MNL model. The basic app featured the most rudimentary levels for all attributes. The second scenario depicted a mobile app currently used to enhance cardiac rehabilitation in specific private cardiac clinics in Queensland, Australia [51].
JMIR Mhealth Uhealth 2025;13:e58556
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Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study
Recent studies have used qualitative and quantitative methods to examine the uptake of Io T and have identified favorable attitudes, ease of use, contentment, affordability, basic knowledge, security, and privacy as crucial factors that influence its adoption.
JMIR Hum Factors 2025;12:e58377
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