Published on in Vol 6, No 3 (2018): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9467, first published .
Satisfying Product Features of a Fall Prevention Smartphone App and Potential Users’ Willingness to Pay: Web-Based Survey Among Older Adults

Satisfying Product Features of a Fall Prevention Smartphone App and Potential Users’ Willingness to Pay: Web-Based Survey Among Older Adults

Satisfying Product Features of a Fall Prevention Smartphone App and Potential Users’ Willingness to Pay: Web-Based Survey Among Older Adults

Journals

  1. Rasche P, Nitsch V, Rentemeister L, Coburn M, Buecking B, Bliemel C, Bollheimer L, Pape H, Knobe M. The Aachen Falls Prevention Scale: Multi-Study Evaluation and Comparison. JMIR Aging 2019;2(1):e12114 View
  2. Wilmink G, Dupey K, Alkire S, Grote J, Zobel G, Fillit H, Movva S. Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study. JMIR Aging 2020;3(2):e19554 View
  3. Zhao X, Wang L, Ge C, Zhen X, Chen Z, Wang J, Zhou Y. Smartphone application training program improves smartphone usage competency and quality of life among the elderly in an elder university in China: A randomized controlled trial. International Journal of Medical Informatics 2020;133:104010 View
  4. Müller S, Lauridsen K, Palic A, Frederiksen L, Mathiasen M, Løfgren B. Mobile App Support for Cardiopulmonary Resuscitation: Development and Usability Study. JMIR mHealth and uHealth 2021;9(1):e16114 View
  5. Hsieh K, Fanning J, Rogers W, Wood T, Sosnoff J. A Fall Risk mHealth App for Older Adults: Development and Usability Study. JMIR Aging 2018;1(2):e11569 View
  6. Bjerkan J, Kane B, Uhrenfeldt L, Veie M, Fossum M. Citizen-Patient Involvement in the Development of mHealth Technology: Protocol for a Systematic Scoping Review. JMIR Research Protocols 2020;9(8):e16781 View
  7. Rabe S, Azhand A, Pommer W, Müller S, Steinert A. Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study. JMIR Aging 2020;3(1):e16131 View
  8. Ogbo E, Brown T, Gant J, Davis A, Sicker D. The Impact of OTT on Preferences for Mobile Services: A Conjoint Analysis of Users in Nigeria. SSRN Electronic Journal 2020 View
  9. Kwon Y, Lee H, Kim W. Design of an IoT-Based Remote Learning System for Medical Skill Training in the Age of COVID-19: Focusing on CPR Skill Training. Applied Sciences 2022;12(17):8840 View
  10. Xie Z, Chen J, Or C. Consumers’ Willingness to Pay for eHealth and Its Influencing Factors: Systematic Review and Meta-analysis. Journal of Medical Internet Research 2022;24(9):e25959 View
  11. Ogbo E, Brown T, Gant J, Davis A, Sicker D. The Impact of Over-the-Top Services on Preferences for Mobile Services: A Conjoint Analysis of Users in Nigeria. Journal of Information Policy 2021;11:403 View
  12. Tanwar R, Nandal N, Zamani M, Manaf A. Pathway of Trends and Technologies in Fall Detection: A Systematic Review. Healthcare 2022;10(1):172 View
  13. Mejía S, Su T, Lan Q, Zou A, Griffin A, Sosnoff J. The Context of Caring and Concern for Falling Differentiate Which Mobile Fall Technology Features Chinese Family Caregivers Find Most Important. Journal of Applied Gerontology 2022;41(4):1175 View
  14. Rasche P, Barton L, Schäfer K, Theis S, Bröhl C, Wille M, Mertens A. Home Use Devices and SaMDs in Patient Self-Care: Concept to Develop Excellenct Products in Digital Health. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2018;7(1):189 View