Published on in Vol 21, No 8 (2019): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15023, first published .
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

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

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

Journals

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  19. Park J, Han J, Kim Y, Rho M. Development, Acceptance, and Concerns Surrounding App-Based Services to Overcome the COVID-19 Outbreak in South Korea: Web-Based Survey Study. JMIR Medical Informatics 2021;9(7):e29315 View
  20. Brew-Sam N, Parkinson A, Chhabra M, Henschke A, Brown E, Pedley L, Pedley E, Hannan K, Brown K, Wright K, Phillips C, Tricoli A, Nolan C, Suominen H, Desborough J. Toward Diabetes Device Development That Is Mindful to the Needs of Young People Living With Type 1 Diabetes: A Data- and Theory-Driven Qualitative Study. JMIR Diabetes 2023;8:e43377 View
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  22. Xu Q, Hou X, Xiao T, Zhao W. Factors Affecting Medical Students’ Continuance Intention to Use Mobile Health Applications. Journal of Multidisciplinary Healthcare 2022;Volume 15:471 View
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  25. 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
  26. Amdie F, Luctkar-Flude M, Snelgrove-Clarke E, Sawhney M, Balcha S, Woo K. Feasibility of Virtual Simulation-Based Diabetes Foot Care Education in Patients with Diabetes in Ethiopia: Protocol for a Randomized Controlled Trial. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2022;Volume 15:995 View
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  28. Alaslawi H, Berrou I, Al Hamid A, Alhuwail D, Aslanpour Z. Diabetes Self-management Apps: Systematic Review of Adoption Determinants and Future Research Agenda. JMIR Diabetes 2022;7(3):e28153 View
  29. Bäuerle A, Frewer A, Rentrop V, Schüren L, Niedergethmann M, Lortz J, Skoda E, Teufel M. Determinants of Acceptance of Weight Management Applications in Overweight and Obese Individuals: Using an Extended Unified Theory of Acceptance and Use of Technology Model. Nutrients 2022;14(9):1968 View
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  31. Gao C, Shen Y, Xu W, Zhang Y, Tu Q, Zhu X, Lu Z, Yang Y. A fuzzy-set qualitative comparative analysis exploration of multiple paths to users’ continuous use behavior of diabetes self-management apps. International Journal of Medical Informatics 2023;172:105000 View
  32. 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
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  35. Howell P, Abdelhamid M. Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study. JMIR Formative Research 2023;7:e36608 View
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  37. Bults M, van Leersum C, Olthuis T, Bekhuis R, den Ouden M. Barriers and Drivers Regarding the Use of Mobile Health Apps Among Patients With Type 2 Diabetes Mellitus in the Netherlands: Explanatory Sequential Design Study. JMIR Diabetes 2022;7(1):e31451 View
  38. 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
  39. 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
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  41. Aigbogun O, Matinari M, Fawehinmi O. Exploring predictors of e-marketing continuance intention in the Zimbabwean pharmaceutical industry during the COVID-19 pandemic. African Journal of Economic and Management Studies 2023;14(3):379 View
  42. 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
  43. Oloveze A, Ugwu P, Okeke V, Chukwuoyims K, Ahaiwe E. Factors motivating end-users’ behavioural intention to recommend m-health innovation: multi-group analysis. Health Economics and Management Review 2022;3(3):17 View
  44. Shen Y, Xu W, Liang A, Wang X, Lu X, Lu Z, Gao C. Online health management continuance and the moderating effect of service type and age difference: A meta-analysis. Health Informatics Journal 2022;28(3) View
  45. Lee S, Choi M, Yu S, Kim H, Park S, Choi I. Development and evaluation of smartphone usage management system for preventing problematic smartphone use. DIGITAL HEALTH 2022;8:205520762210890 View
  46. Zha H, Liu K, Tang T, Yin Y, Dou B, Jiang L, Yan H, Tian X, Wang R, Xie W. Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: an extension of the UTAUT model. BMC Medical Informatics and Decision Making 2022;22(1) View
  47. Chang I, Shih Y, Kuo K. Why would you use medical chatbots? interview and survey. International Journal of Medical Informatics 2022;165:104827 View
  48. Chen J, Wijesundara J, Enyim G, Lombardini L, Gerber B, Houston T, Sadasivam R. Understanding Patients’ Intention to Use Digital Health Apps That Support Postdischarge Symptom Monitoring by Providers Among Patients With Acute Coronary Syndrome: Survey Study. JMIR Human Factors 2022;9(1):e34452 View
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  54. Walsh P, Singh R. Determinants of Millennial behaviour towards current and future use of video streaming services. Young Consumers 2022;23(3):397 View
  55. Liu Y, Hao H, Sharma M, Harris Y, Scofi J, Trepp R, Farmer B, Ancker J, Zhang Y. Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals. Applied Clinical Informatics 2022;13(02):447 View
  56. Senteio C, Murdock P. The Efficacy of Health Information Technology in Supporting Health Equity for Black and Hispanic Patients With Chronic Diseases: Systematic Review. Journal of Medical Internet Research 2022;24(4):e22124 View
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  59. 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
  60. Al-Bashayreh M, Almajali D, Al-Okaily M, Masa’deh R, Samed Al-Adwan A. Evaluating Electronic Customer Relationship Management System Success: The Mediating Role of Customer Satisfaction. Sustainability 2022;14(19):12310 View
  61. 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
  62. Rahimi R, Khoundabi B, fathian A. Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens. International Journal of Medical Informatics 2021;155:104571 View
  63. Xie Q, Hu X, Wang Y, Peng J, Cheng L. Exploration of the health needs of patients with poorly controlled type 2 diabetes using a user-centred co-production approach in the area of mHealth: an exploratory sequential mixed-method protocol. BMJ Open 2022;12(12):e063814 View
  64. 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
  65. Yao Y, Li Z, He Y, Zhang Y, Guo Z, Lei Y, Zhao Q, Li D, Zhang Z, Zhang Y, Liao X. Factors affecting wearable ECG device adoption by general practitioners for atrial fibrillation screening: cross-sectional study. Frontiers in Public Health 2023;11 View
  66. 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
  67. Qu S, Zhou M, Kong N, Campy K. Factors influencing user acceptance of weight management apps among Chinese obese individuals during the COVID-19 pandemic. Health Policy and Technology 2023;12(2):100758 View
  68. Koo J, Park Y, Kang D. Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study. JMIR Aging 2023;6:e41429 View
  69. Stehr P, Ermel L, Rossmann C, Reifegerste D, Lindemann A, Schulze A. A Mobile Health Information Behavior Model: Theoretical Development and Mixed-Method Testing in the Context of Mobile Apps on Child Poisoning Prevention. Journal of Health Communication 2023;28(10):648 View
  70. Kruger S, Deacon E, van Rensburg E, Segal D. Identification of psychological constructs for a positive psychology intervention to assist with the adjustment to closed loop technology among adolescents living with type 1 diabetes. Frontiers in Psychology 2023;14 View
  71. Bouteraa M, Al-Daihani M, Chekima B, Ansar R, Tamma E, Lada S, Baddou A, Elkheloufi A, Fook L. A Multi-Analytical Approach to Investigate the Motivations of Sustainable Green Technology in the Banking Industry. International Journal of Social Ecology and Sustainable Development 2023;15(1):1 View
  72. Brunet J, Sharma S, Price J, Black M. Acceptability and Usability of a Theory-Driven Intervention via Email to Promote Physical Activity in Women Who Are Overweight or Obese: Substudy Within a Randomized Controlled Trial. JMIR Formative Research 2023;7:e48301 View
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