Published on in Vol 5, No 12 (2017): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/7886, first published .
Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Journals

  1. Apolinário-Hagen J, Menzel M, Hennemann S, Salewski C. Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study. JMIR Formative Research 2018;2(2):e11977 View
  2. Duan H, Wang Z, Ji Y, Ma L, Liu F, Chi M, Deng N, An J. Using Goal-Directed Design to Create a Mobile Health App to Improve Patient Compliance With Hypertension Self-Management: Development and Deployment. JMIR mHealth and uHealth 2020;8(2):e14466 View
  3. Chen J, Allman-Farinelli M. Impact of Training and Integration of Apps Into Dietetic Practice on Dietitians’ Self-Efficacy With Using Mobile Health Apps and Patient Satisfaction. JMIR mHealth and uHealth 2019;7(3):e12349 View
  4. Salgado T, Tavares J, Oliveira T. Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development. JMIR mHealth and uHealth 2020;8(7):e17588 View
  5. Onuma A, Palmer Kelly E, Chakedis J, Paredes A, Tsilimigras D, Wiemann B, Johnson M, Merath K, Akgul O, Cloyd J, Pawlik T. Patient preferences on the use of technology in cancer surveillance after curative surgery: A cross-sectional analysis. Surgery 2019;165(4):782 View
  6. Ali R, Zhang Z, Soomro M. Smoking-Cessation Acceptance Via Mobile Health and Quick Response Code Technologies: Empirical Evidence of a Pilot Study from China and Pakistan. Current Psychology 2021;40(12):6085 View
  7. Wang Z, Huang H, Cui L, Chen J, An J, Duan H, Ge H, Deng N. Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System. JMIR Medical Informatics 2020;8(4):e17642 View
  8. Thongtipmak S, Buranruk O, Eungpinichpong W, Konharn K. Immediate Effects and Acceptability of an Application-Based Stretching Exercise Incorporating Deep Slow Breathing for Neck Pain Self-management. Healthcare Informatics Research 2020;26(1):50 View
  9. Kabeza C, Harst L, Schwarz P, Timpel P. A qualitative study of users’ experiences after 3 months: the first Rwandan diabetes self-management Smartphone application “Kir’App”. Therapeutic Advances in Endocrinology and Metabolism 2020;11:204201882091451 View
  10. Rodriguez Hermosa J, Fuster Gomila A, Puente Maestu L, Amado Diago C, Callejas González F, Malo De Molina Ruiz R, Fuentes Ferrer M, Álvarez Sala-Walther J, Calle Rubio M. Compliance and Utility of a Smartphone App for the Detection of Exacerbations in Patients With Chronic Obstructive Pulmonary Disease: Cohort Study. JMIR mHealth and uHealth 2020;8(3):e15699 View
  11. Melchiorre M, Lamura G, Barbabella F, MacLure K. eHealth for people with multimorbidity: Results from the ICARE4EU project and insights from the “10 e’s” by Gunther Eysenbach. PLOS ONE 2018;13(11):e0207292 View
  12. Cher B, Kembhavi G, Toh K, Audimulam J, Chia W, Vrijhoef H, Lim Y, Lim T. Understanding the Attitudes of Clinicians and Patients Toward a Self-Management eHealth Tool for Atrial Fibrillation: Qualitative Study. JMIR Human Factors 2020;7(3):e15492 View
  13. Ali R, Zhang Z, Bux Soomro M. Smoking-cessation acceptance via mobile health. Human Systems Management 2019;38(3):313 View
  14. C.C. S, Prathap S. Continuance adoption of mobile-based payments in Covid-19 context: an integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications 2020;16(4):351 View
  15. Balapour A, Reychav I, Sabherwal R, Azuri J. Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps. International Journal of Information Management 2019;49:58 View
  16. Chang C. Exploring the Usage Intentions of Wearable Medical Devices: A Demonstration Study. Interactive Journal of Medical Research 2020;9(3):e19776 View
  17. Nadal C, Sas C, Doherty G. Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement. Journal of Medical Internet Research 2020;22(7):e17256 View
  18. Harst L, Lantzsch H, Scheibe M. Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review. Journal of Medical Internet Research 2019;21(5):e13117 View
  19. Madrigal L, Escoffery C. Electronic Health Behaviors Among US Adults With Chronic Disease: Cross-Sectional Survey. Journal of Medical Internet Research 2019;21(3):e11240 View
  20. Tang Y, Yang Y, Shao Y. Acceptance of Online Medical Websites: An Empirical Study in China. International Journal of Environmental Research and Public Health 2019;16(6):943 View
  21. Ye T, Xue J, He M, Gu J, Lin H, Xu B, Cheng Y. Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study. Journal of Medical Internet Research 2019;21(10):e14316 View
  22. Zhang Y, Liu C, Luo S, Xie Y, Liu F, Li X, Zhou Z. Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey. Journal of Medical Internet Research 2019;21(8):e15023 View
  23. Ye Q, Deng Z, Chen Y, Liao J, Li G, Lu Y. How Resource Scarcity and Accessibility Affect Patients’ Usage of Mobile Health in China: Resource Competition Perspective. JMIR mHealth and uHealth 2019;7(8):e13491 View
  24. Li J, Chang X. Improving mobile health apps usage: a quantitative study on mPower data of Parkinson's disease. Information Technology & People 2021;34(1):399 View
  25. Lee M, Kang D, Yoon J, Shim S, Kim I, Oh D, Shin S, Hesse B, Cho J, Weston K. The difference in knowledge and attitudes of using mobile health applications between actual user and non-user among adults aged 50 and older. PLOS ONE 2020;15(10):e0241350 View
  26. Zhang Z, Zhang L, Zheng J, Xiao H, Li Z. COVID-19–Related Disruptions and Increased mHealth Emergency Use Intention: Experience Sampling Method Study. JMIR mHealth and uHealth 2020;8(12):e20642 View
  27. Li D, Hu Y, Pfaff H, Wang L, Deng L, Lu C, Xia S, Cheng S, Zhu X, Wu X. Determinants of Patients’ Intention to Use the Online Inquiry Services Provided by Internet Hospitals: Empirical Evidence From China. Journal of Medical Internet Research 2020;22(10):e22716 View
  28. Binyamin S, Zafar B. Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis. Health Informatics Journal 2021;27(1):146045822097673 View
  29. Schleicher M, Unnikrishnan V, Neff P, Simoes J, Probst T, Pryss R, Schlee W, Spiliopoulou M. Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring. Scientific Reports 2020;10(1) View
  30. Deng N, Chen J, Liu Y, Wei S, Sheng L, Lu R, Wang Z, Zhu J, An J, Wang B, Lin H, Wang X, Zhou Y, Duan H, Ran P. Using Mobile Health Technology to Deliver a Community-Based Closed-Loop Management System for Chronic Obstructive Pulmonary Disease Patients in Remote Areas of China: Development and Prospective Observational Study. JMIR mHealth and uHealth 2020;8(11):e15978 View
  31. Yamada J, Kouri A, Simard S, Segovia S, Gupta S. Barriers and Enablers to Using a Patient-Facing Electronic Questionnaire: A Qualitative Theoretical Domains Framework Analysis. Journal of Medical Internet Research 2020;22(10):e19474 View
  32. Alrumayh A, Lehman S, Tan C. Emerging mobile apps: challenges and open problems. CCF Transactions on Pervasive Computing and Interaction 2021;3(1):57 View
  33. Pan M, Gao W. Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research 2021;21(1) View
  34. Russ S, Sevdalis N, Ocloo J. A Smartphone App Designed to Empower Patients to Contribute Toward Safer Surgical Care: Qualitative Evaluation of Diverse Public and Patient Perceptions Using Focus Groups. JMIR mHealth and uHealth 2021;9(4):e24065 View
  35. Nadal C, Earley C, Enrique A, Vigano N, Sas C, Richards D, Doherty G. Integration of a smartwatch within an internet-delivered intervention for depression: Protocol for a feasibility randomized controlled trial on acceptance. Contemporary Clinical Trials 2021;103:106323 View
  36. Fareed N, Jonnalagadda P, MacEwan S, Di Tosto G, Scarborough S, Huerta T, McAlearney A. Differential Effects of Outpatient Portal User Status on Inpatient Portal Use: Observational Study. Journal of Medical Internet Research 2021;23(4):e23866 View
  37. Song T, Deng N, Cui T, Qian S, Liu F, Guan Y, Yu P. Measuring Success of Patients’ Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation. Journal of Medical Internet Research 2021;23(7):e26670 View
  38. Daragmeh A, Sági J, Zéman Z. Continuous Intention to Use E-Wallet in the Context of the COVID-19 Pandemic: Integrating the Health Belief Model (HBM) and Technology Continuous Theory (TCT). Journal of Open Innovation: Technology, Market, and Complexity 2021;7(2):132 View
  39. Song T, Yu P, Bliokas V, Probst Y, Peoples G, Qian S, Houston L, Perez P, Amirghasemi M, Cui T, Hitige N, Smith N. Clinician-Led, Experience-Based Co-Design Approach for Developing Mobile Health Services to Support Patient Self-Management of Chronic Conditions: Development and a Design Case (Preprint). JMIR mHealth and uHealth 2020 View
  40. Volpi S, Biduski D, Bellei E, Tefili D, McCleary L, Alves A, De Marchi A. Using a mobile health app to improve patients’ adherence to hypertension treatment: a non-randomized clinical trial. PeerJ 2021;9:e11491 View
  41. Huang Y, Trinh M, Le T. Critical Factors Affecting Intention of Use of Augmented Hearing Protection Technology in Construction. Journal of Construction Engineering and Management 2021;147(8) View
  42. Wattanapisit A, Amaek W, Wattanapisit S, Tuangratananon T, Wongsiri S, Pengkaew P. Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study. International Journal of General Medicine 2021;Volume 14:3821 View
  43. Zhai H, Yang X, Xue J, Lavender C, Ye T, Li J, Xu L, Lin L, Cao W, Sun Y. Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study. Journal of Medical Internet Research 2021;23(9):e27122 View
  44. Ciccone N, Dornonville de la Cour F, Thorpe J, Forchhammer B, Maier A. PERSONAL TECHNOLOGY USE AMONGST STROKE PATIENTS: UNDERSTANDING THE BEST PLATFORMS FOR THE DESIGN OF HEALTH INTERVENTIONS IN TREATMENT AND REHABILITATION. Proceedings of the Design Society 2021;1:2419 View
  45. Lee M, Kang D, Kim S, Lim J, Yoon J, Kim Y, Shim S, Kang E, Ahn J, Cho J, Shin S, Oh D. Who is more likely to adopt and comply with the electronic patient-reported outcome measure (ePROM) mobile application? A real-world study with cancer patients undergoing active treatment. Supportive Care in Cancer 2022;30(1):659 View
  46. Wu D, An J, Yu P, Lin H, Ma L, Duan H, Deng N. Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis. Journal of Medical Internet Research 2021;23(9):e25630 View
  47. Guazzini A, Fiorenza M, Panerai G, Duradoni M. What Went Wrong? Predictors of Contact Tracing Adoption in Italy during COVID-19 Pandemic. Future Internet 2021;13(11):286 View
  48. Na S, Heo S, Han S, Shin Y, Roh Y. Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings 2022;12(2):90 View
  49. Viana Pereira F, Tavares J, Oliveira T. Adoption of video consultations during the COVID-19 pandemic. Internet Interventions 2023;31:100602 View
  50. Jacobs-Basadien M, Pather S, Petersen F. The role of culture in the adoption of mobile applications for the self-management of diabetes in low resourced urban communities. Universal Access in the Information Society 2024;23(2):743 View
  51. Zhang Z, Vaghefi I. Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model. JMIR Formative Research 2022;6(12):e40302 View
  52. Deng N, Sheng L, Jiang W, Hao Y, Wei S, Wang B, Duan H, Chen J. A home-based pulmonary rehabilitation mHealth system to enhance the exercise capacity of patients with COPD: development and evaluation. BMC Medical Informatics and Decision Making 2021;21(1) View
  53. Mukherjee J. Adoption of personal service robots in India. IIMB Management Review 2022;34(4):378 View
  54. Liu K, Tao D. The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior 2022;127:107026 View
  55. Liu K, Or C, So M, Cheung B, Chan B, Tiwari A, Tan J. A longitudinal examination of tablet self-management technology acceptance by patients with chronic diseases: Integrating perceived hand function, perceived visual function, and perceived home space adequacy with the TAM and TPB. Applied Ergonomics 2022;100:103667 View
  56. Breil B, Salewski C, Apolinário-Hagen J. Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey. JMIR Cardio 2022;6(1):e31617 View
  57. Sabbir M, Taufique K, Nomi M. Telemedicine acceptance during the COVID-19 pandemic: User satisfaction and strategic healthcare marketing considerations. Health Marketing Quarterly 2021;38(2-3):168 View
  58. Schretzlmaier P, Hecker A, Ammenwerth E. Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study. BMJ Health & Care Informatics 2022;29(1):e100640 View
  59. Passalent L, Cyr A, Jurisica I, Mathur S, Inman R, Haroon N. Motivators, Barriers, and Opportunity for E‐Health to Encourage Physical Activity in Axial Spondyloarthritis: A Qualitative Descriptive Study. Arthritis Care & Research 2022;74(1):50 View
  60. Zamil A, Ali S, Poulova P, Akbar M. An ounce of prevention or a pound of cure? Multi-level modelling on the antecedents of mobile-wallet adoption and the moderating role of e-WoM during COVID-19. Frontiers in Psychology 2022;13 View
  61. Harakeh Z, Van Keulen H, Hogenelst K, Otten W, De Hoogh I, Van Empelen P. Predictors of the Acceptance of an Electronic Coach Targeting Self-management of Patients With Type 2 Diabetes: Web-Based Survey. JMIR Formative Research 2022;6(8):e34737 View
  62. Gao Y, Gong L, Liu H, Kong Y, Wu X, Guo Y, Hu D. Research on the influencing factors of users’ information processing in online health communities based on heuristic-systematic model. Frontiers in Psychology 2022;13 View
  63. De Regge M, Van Caelenberg E, Van Belle N, Eeckloo K, Coppens M. Encouraging Digital Patient Portal Use in Ambulatory Surgery: A Mixed Method Research of Patients and Health Care Professionals Experiences and Perceptions. Journal of PeriAnesthesia Nursing 2022;37(5):691 View
  64. Silva G, Dias Á, Rodrigues M. Continuity of Use of Food Delivery Apps: An Integrated Approach to the Health Belief Model and the Technology Readiness and Acceptance Model. Journal of Open Innovation: Technology, Market, and Complexity 2022;8(3):114 View
  65. Schretzlmaier P, Hecker A, Ammenwerth E. 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. JMIR Human Factors 2022;9(1):e34918 View
  66. Van Rhoon L, McSharry J, Byrne M. Development and testing of a digital health acceptability model to explain the intention to use a digital diabetes prevention programme. British Journal of Health Psychology 2022;27(3):716 View
  67. Van Baelen F, De Regge M, Larivière B, Verleye K, Schelfout S, Eeckloo K. Role of Social and App-Related Factors in Behavioral Engagement With mHealth for Improved Well-being Among Chronically Ill Patients: Scenario-Based Survey Study. JMIR mHealth and uHealth 2022;10(8):e33772 View
  68. Tahsin F, Austin T, McKinstry B, Mercer S, Loganathan M, Thavorn K, Upshur R, Steele Gray C. Examining Use Behavior of a Goal-Supporting mHealth App in Primary Care Among Patients With Multiple Chronic Conditions: Qualitative Descriptive Study. JMIR Human Factors 2022;9(4):e37684 View
  69. Lin-Hi N, Haensse L, Hollands L, Blumberg I. The role of ethics in technology acceptance: analysing resistance to new health technologies on the example of a COVID-19 contact-tracing app. Journal of Decision Systems 2024;33(1):164 View
  70. Zhu Z, Liu Y, Cao X, Dong W. Factors Affecting Customer Intention to Adopt a Mobile Chronic Disease Management Service. Journal of Organizational and End User Computing 2021;34(4):1 View
  71. Xu L, Li P, Hou X, Yu H, Tang T, Liu T, Xiang S, Wu X, Huang C. Middle-aged and elderly users’ continuous usage intention of health maintenance-oriented WeChat official accounts: empirical study based on a hybrid model in China. BMC Medical Informatics and Decision Making 2021;21(1) View
  72. Le X. The diffusion of mobile QR-code payment: an empirical evaluation for a pandemic. Asia-Pacific Journal of Business Administration 2022;14(4):617 View
  73. Mendez K, Budhathoki C, Labrique A, Sadak T, Tanner E, Han H. Factors Associated With Intention to Adopt mHealth Apps Among Dementia Caregivers With a Chronic Condition: Cross-sectional, Correlational Study. JMIR mHealth and uHealth 2021;9(8):e27926 View
  74. Dwairej L, Ahmad M. Hypertension and mobile application for self-care, self-efficacy and related knowledge. Health Education Research 2022;37(3):199 View
  75. Kang S, Baek H, Cho J, Kim S, Hwang H, Lee W, Park J, Yoon Y, Yoon C, Cho Y, Youn T, Cho G, Chae I, Choi D, Yoo S, Suh J. Management of cardiovascular disease using an mHealth tool: a randomized clinical trial. npj Digital Medicine 2021;4(1) View
  76. Klaver N, van de Klundert J, van den Broek R, Askari M. Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study. JMIR mHealth and uHealth 2021;9(8):e26845 View
  77. Addotey-Delove M, Scott R, Mars M. Healthcare Workers’ Perspectives of mHealth Adoption Factors in the Developing World: Scoping Review. International Journal of Environmental Research and Public Health 2023;20(2):1244 View
  78. AlQudah A, Al-Emran M, Shaalan K. Technology Acceptance in Healthcare: A Systematic Review. Applied Sciences 2021;11(22):10537 View
  79. Martin-Payo R, Carrasco-Santos S, Cuesta M, Stoyan S, Gonzalez-Mendez X, Fernandez-Alvarez M. Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS). Journal of the American Medical Informatics Association 2021;28(12):2681 View
  80. Chopdar P. Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator. Health Policy and Technology 2022;11(3):100651 View
  81. Zahed K, Fields S, Sasangohar F. Investigating the Efficacy of Behavioral Models to Predict Use of Health Technology: A Scoping Review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022;66(1):2172 View
  82. Crouch J, Winters K, Zhang L, Stewart M. Telehealth during the pandemic: Patient perceptions and policy implications. Journal of Nursing Scholarship 2023;55(1):141 View
  83. Yoo J, Park J, Park H. Understanding VR-Based Construction Safety Training Effectiveness: The Role of Telepresence, Risk Perception, and Training Satisfaction. Applied Sciences 2023;13(2):1135 View
  84. Drabarek D, Habgood E, Janda M, Hersch J, Ackermann D, Low D, Low C, Morton R, Dieng M, Cust A, Morgan A, Smith E, Bell K. Experiences of Patient-Led Surveillance, Including Patient-Performed Teledermoscopy, in the MEL-SELF Pilot Randomized Controlled Trial: Qualitative Interview Study. JMIR Dermatology 2022;5(3):e35916 View
  85. Hasan N, Bao Y, Chiong R. A multi-method analytical approach to predicting young adults’ intention to invest in mHealth during the COVID-19 pandemic. Telematics and Informatics 2022;68:101765 View
  86. Benedict C, Lazard A, Smith S, Agrawal A, Collins M, Love B. User experiences, usability, and social presence of a peer-to-peer support app: survey of young adults affected by cancer. Journal of Applied Communication Research 2021;49(5):497 View
  87. Ghozali M, Dewi P, Trisnawati . Implementing the technology acceptance model to examine user acceptance of the asthma control test app. International Journal of System Assurance Engineering and Management 2022;13(S1):742 View
  88. Junker M, Böhm M, Franz M, Fritsch T, Krcmar H. Value of normative belief in intention to use workplace health promotion apps. BMC Medical Informatics and Decision Making 2022;22(1) View
  89. Raman P, Aashish K. Influence of COVID-19 pandemic on the intention to adopt mobile payment systems in India. Qualitative Market Research: An International Journal 2023;26(4):368 View
  90. Zahed K, Smith A, McDonald A, Sasangohar F. The Effects of Drowsiness Detection Technology and Education on Nurses’ Beliefs and Attitudes toward Drowsy Driving. IISE Transactions on Occupational Ergonomics and Human Factors 2022;10(2):104 View
  91. Marto A, Gonçalves A, Melo M, Bessa M, Silva R. ARAM: A Technology Acceptance Model to Ascertain the Behavioural Intention to Use Augmented Reality. Journal of Imaging 2023;9(3):73 View
  92. Kerr J, Naegelin M, Benk M, v Wangenheim F, Meins E, Viganò E, Ferrario A. Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study. Journal of Medical Internet Research 2023;25:e44131 View
  93. Al-Adwan A, Li N, Al-Adwan A, Abbasi G, Albelbisi N, Habibi A. “Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms”. Education and Information Technologies 2023;28(11):15381 View
  94. Na S, Heo S, Choi W, Han S, Kim C. Firm Size and Artificial Intelligence (AI)-Based Technology Adoption: The Role of Corporate Size in South Korean Construction Companies. Buildings 2023;13(4):1066 View
  95. Zahed K, Mehta R, Erraguntla M, Qaraqe K, Sasangohar F. Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample. JMIR Diabetes 2023;8:e41501 View
  96. Ajina A, Javed H, Ali S, Zamil A. Are men from mars, women from venus? Examining gender differences of consumers towards mobile-wallet adoption during pandemic. Cogent Business & Management 2023;10(1) View
  97. Na S, Heo S, Choi W, Kim C, Whang S. Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model. Buildings 2023;13(10):2518 View
  98. Almathami H, Win K, Vlahu-Gjorgievska E. Empirical Evidence of Internal and External Factors Influencing Users' Motivation Toward Teleconsultation Use. Telemedicine and e-Health 2023 View
  99. Hendricks J, Smith A, Peres S, Sasangohar F. Workers’ Acceptance of Digital Procedures: An Application of the Technology Acceptance Model. IISE Transactions on Occupational Ergonomics and Human Factors 2023;11(1-2):59 View
  100. Barua Z, Barua A. Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural network approach. Journal of Migration and Health 2023;8:100201 View
  101. Shao H, Liu C, Tang L, Wang B, Xie H, Zhang Y. Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study. JMIR Human Factors 2023;10:e46624 View
  102. Li T, Zhang Y, Luo X, Wan W, Zhang H, Wang X, Wang D. Exploring Patients' Intentions for Usage of Video Telemedicine Follow-Up Services: Cross-Sectional Study. Telemedicine and e-Health 2024;30(3):731 View
  103. Kuen L, Schürmann F, Westmattelmann D, Hartwig S, Tzafrir S, Schewe G. Trust transfer effects and associated risks in telemedicine adoption. Electronic Markets 2023;33(1) View
  104. Zahed K, Markert C, Dunn P, Sasangohar F. Investigating the effect of an mHealth coaching intervention on health beliefs, adherence and blood pressure of patients with hypertension: A longitudinal single group pilot study. DIGITAL HEALTH 2023;9 View
  105. Nadal C, Earley C, Enrique A, Sas C, Richards D, Doherty G. Patient Acceptance of Self-Monitoring on a Smartwatch in a Routine Digital Therapy: A Mixed-Methods Study. ACM Transactions on Computer-Human Interaction 2024;31(1):1 View
  106. Azam M, Bin Naeem S, Kamel Boulos M, Faiola A. Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments. International Journal of Environmental Research and Public Health 2023;20(23):7112 View
  107. Chen H, Li H, Li L, Zhang X, Gu J, Wang Q, Wu C, Wu Y. Factors Associated with Intention to Use Telerehabilitation for Children with Special Needs: A Cross-Sectional Study. Telemedicine and e-Health 2024;30(5):1425 View
  108. Chen B, Chang Y, Wang B, Zou J, Tu S. Technology acceptance model perspective on the intention to participate in medical talents training in China. Heliyon 2024;10(4):e26206 View
  109. Swartjes H, Aarts C, Deuning-Smit E, Vromen H, de Wilt J, Vos J, Custers J. Patient experiences with patient-led, home-based follow-up after curative treatment for colorectal cancer: a qualitative study. BMJ Open 2024;14(2):e081655 View
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  11. Jacobs-Basadien M, Pather S. Information and Communication Technologies for Ageing Well and e-Health. View
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