Published on 06.12.17 in Vol 5, No 12 (2017): December
Works citing "Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test"
According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.7886):
(note that this is only a small subset of citations)
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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
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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
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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
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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
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Onuma AE, Palmer Kelly E, Chakedis J, Paredes AZ, Tsilimigras DI, Wiemann B, Johnson M, Merath K, Akgul O, Cloyd J, Pawlik TM. Patient preferences on the use of technology in cancer surveillance after curative surgery: A cross-sectional analysis. Surgery 2019;165(4):782
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Ali R, Zhang Z, Soomro MB. 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
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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
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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
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Kabeza CB, Harst L, Schwarz PE, 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
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Rodriguez Hermosa JL, Fuster Gomila A, Puente Maestu L, Amado Diago CA, Callejas González FJ, Malo De Molina Ruiz R, Fuentes Ferrer ME, Álvarez Sala-Walther JL, 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
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Melchiorre MG, 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
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Cher BP, Kembhavi G, Toh KY, Audimulam J, Chia WA, Vrijhoef HJ, Lim YW, Lim TW. 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
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Ali R, Zhang Z, Bux Soomro M. Smoking-cessation acceptance via mobile health. Human Systems Management 2019;38(3):313
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C.C. S, Prathap SK. 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
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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
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. Exploring the Usage Intentions of Wearable Medical Devices: A Demonstration Study. Interactive Journal of Medical Research 2020;9(3):e19776
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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
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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
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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
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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
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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
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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
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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
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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
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Lee M, Kang D, Yoon J, Shim S, Kim I, Oh D, Shin S, Hesse BW, Cho J, Weston KL. 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
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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
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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
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Binyamin SS, Zafar BA. 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
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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)
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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
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Yamada J, Kouri A, Simard S, Segovia SA, 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
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Alrumayh AS, Lehman SM, Tan CC. Emerging mobile apps: challenges and open problems. CCF Transactions on Pervasive Computing and Interaction 2021;3(1):57
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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)
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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
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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
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Fareed N, Jonnalagadda P, MacEwan SR, Di Tosto G, Scarborough S, Huerta TR, McAlearney AS. Differential Effects of Outpatient Portal User Status on Inpatient Portal Use: Observational Study. Journal of Medical Internet Research 2021;23(4):e23866
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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
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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
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Song T, Yu P, Bliokas V, Probst Y, Peoples GE, Qian S, Houston L, Perez P, Amirghasemi M, Cui T, Hitige NPR, Smith NA. 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;
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Volpi SS, Biduski D, Bellei EA, Tefili D, McCleary L, Alves ALS, De Marchi ACB. Using a mobile health app to improve patients’ adherence to hypertension treatment: a non-randomized clinical trial. PeerJ 2021;9:e11491
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Huang Y, Trinh MT, Le T. Critical Factors Affecting Intention of Use of Augmented Hearing Protection Technology in Construction. Journal of Construction Engineering and Management 2021;147(8)
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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
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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
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Ciccone NW, Dornonville de la Cour FL, Thorpe JR, Forchhammer BH, 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
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Lee M, Kang D, Kim S, Lim J, Yoon J, Kim Y, Shim S, Kang E, Ahn JS, 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
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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
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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
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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
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Viana Pereira F, Tavares J, Oliveira T. Adoption of video consultations during the COVID-19 pandemic. Internet Interventions 2023;31:100602
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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 2022;
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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
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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)
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. Adoption of personal service robots in India. IIMB Management Review 2022;34(4):378
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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
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Liu K, Or CK, 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
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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
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Sabbir MM, Taufique KMR, Nomi M. Telemedicine acceptance during the COVID-19 pandemic: User satisfaction and strategic healthcare marketing considerations. Health Marketing Quarterly 2021;38(2-3):168
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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 Online 2022;29(1):e100640
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Passalent L, Cyr A, Jurisica I, Mathur S, Inman RD, 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
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Zamil AMA, 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
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Harakeh Z, Van Keulen H, Hogenelst K, Otten W, De Hoogh IM, 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
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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
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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
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Silva GM, Dias , Rodrigues MS. 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
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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
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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
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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
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Tahsin F, Austin T, McKinstry B, Mercer SW, 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
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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
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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
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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)
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. The diffusion of mobile QR-code payment: an empirical evaluation for a pandemic. Asia-Pacific Journal of Business Administration 2022;14(4):617
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Mendez KJW, Budhathoki C, Labrique AB, Sadak T, Tanner EK, Han HR. 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
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Dwairej L, Ahmad M. Hypertension and mobile application for self-care, self-efficacy and related knowledge. Health Education Research 2022;37(3):199
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Kang S, Baek H, Cho J, Kim S, Hwang H, Lee W, Park JJ, Yoon YE, 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)
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Klaver NS, van de Klundert J, van den Broek RJGM, 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
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Addotey-Delove M, Scott RE, 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
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AlQudah AA, Al-Emran M, Shaalan K. Technology Acceptance in Healthcare: A Systematic Review. Applied Sciences 2021;11(22):10537
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Martin-Payo R, Carrasco-Santos S, Cuesta M, Stoyan S, Gonzalez-Mendez X, Fernandez-Alvarez MDM. 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
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Al-Adwan AS, Li N, Al-Adwan A, Abbasi GA, Albelbisi NA, 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
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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
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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
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Swartjes H, Aarts CJH, Deuning-Smit E, Vromen HAB, de Wilt JHW, Vos JAM, Custers JAE. Patient experiences with patient-led, home-based follow-up after curative treatment for colorectal cancer: a qualitative study. BMJ Open 2024;14(2):e081655
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According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.7886):
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Yu P, Zhu Y, Halim UZ, Hailey D. Encyclopedia of Gerontology and Population Aging. 2019. Chapter 440-1:1
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Spohn V, Rettegi L, Spohn L. mHealth-Anwendungen für chronisch Kranke. 2020. Chapter 5:63
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Tong Y, Yan H, Li X, Chen G, Zhang Z. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. 2021. Chapter 10:100
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Burkard M, Lockl J, Zürl T, Ruhland N. Innovation Through Information Systems. 2021. Chapter 4:55
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Yu P, Zhu Y, Halim UZ, Hailey D. Encyclopedia of Gerontology and Population Aging. 2021. Chapter 440:1164
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