The acceptance of personal health devices among patients with chronic conditions

BACKGROUND Personal health devices (PHDs) are rapidly developing and getting smarter. But little is known about chronic patients' acceptance of such PHDs. OBJECTIVE The objective of this study is to explore how chronic patients accept PHDs and what are the main factors that predict use intention of PHDs. The results will provide suggestions for the design of PHDs and e-health services. METHOD A questionnaire survey was conducted to identify the main factors that affect chronic patients' acceptance of PHDs. Three hundred and forty-six valid responses from chronic patients were collected and the data were analyzed using exploratory factor analysis and regression analysis method. The questionnaire also included questions about respondents' experience of PHDs and preference of PHD functions. These questions help to understand lived experience of PHD users and to explain the factors that influence their use intention. RESULT Five influencing factors that predict use intention of PHDs were identified: attitude toward technology, perceived usefulness, ease of learning and availability, social support, and perceived pressure. An acceptance model of PHDs was proposed based on these factors, and suggestions for PHD designers and e-health service designers were discussed. The exploration of PHD experience indicated that ease of learning and social norm significantly influenced PHD use intention, and many respondents expressed negative opinions on the accuracy, durability and maintenance service of PHDs. Besides, people generally expressed positive attitude toward future functions of a PHD.

[1]  Mandy Scheermesser,et al.  User acceptance of pervasive computing in healthcare: Main findings of two case studies , 2008, Pervasive 2008.

[2]  M. Jung,et al.  Acceptance of Swedish e-health services , 2010, Journal of multidisciplinary healthcare.

[3]  Ronald E. Rice,et al.  Public views of mobile medical devices and services: A US national survey of consumer sentiments towards RFID healthcare technology , 2009, Int. J. Medical Informatics.

[4]  Nuria Oliver,et al.  HealthGear: a real-time wearable system for monitoring and analyzing physiological signals , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[5]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[6]  Jui-Chen Huang,et al.  Innovative health care delivery system - A questionnaire survey to evaluate the influence of behavioral factors on individuals' acceptance of telecare , 2013, Comput. Biol. Medicine.

[7]  Martina Ziefle,et al.  Acceptance of pervasive healthcare systems: A comparison of different implementation concepts , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[8]  E. Rogers Diffusion of Innovations , 1962 .

[9]  Joost van Hoof,et al.  Factors influencing acceptance of technology for aging in place: A systematic review , 2014, Int. J. Medical Informatics.

[10]  L. F. Frey Law,et al.  Mathematical models use varying parameter strategies to represent paralyzed muscle force properties: a sensitivity analysis , 2005, Journal of NeuroEngineering and Rehabilitation.

[11]  Fred D. Davis A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[12]  Alex Pentland,et al.  Wearable feedback systems for rehabilitation , 2005, Journal of NeuroEngineering and Rehabilitation.

[13]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[14]  I. Ajzen The theory of planned behavior , 1991 .

[15]  Renato Pietro Ricci,et al.  Long-term patient acceptance of and satisfaction with implanted device remote monitoring. , 2010, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[16]  Bor-Shing Lin,et al.  RTWPMS: A Real-Time Wireless Physiological Monitoring System , 2006, IEEE Transactions on Information Technology in Biomedicine.

[17]  Paul Lukowicz,et al.  AMON: a wearable multiparameter medical monitoring and alert system , 2004, IEEE Transactions on Information Technology in Biomedicine.

[18]  Paolo De Stefanis,et al.  Patient‐centred cardio vascular disease management – end‐user perceptions , 2012 .

[19]  Martina Ziefle,et al.  Technical Expertise and Its Influence on the Acceptance of Future Medical Technologies: What Is Influencing What to Which Extent? , 2010, USAB.

[20]  J. Habetha The myheart project - Fighting cardiovascular diseases by prevention and early diagnosis , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Klaus Miesenberger,et al.  HCI and Usability for e-Inclusion, 5th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society, USAB 2009, Linz, Austria, November 9-10, 2009 Proceedings , 2009, USAB.

[22]  Mariusz Duplaga,et al.  The acceptance of e-health solutions among patients with chronic respiratory conditions. , 2013, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[23]  Zhanpeng Jin,et al.  HeartToGo: A Personalized medicine technology for cardiovascular disease prevention and detection , 2009, 2009 IEEE/NIH Life Science Systems and Applications Workshop.

[24]  Andreas Holzinger,et al.  HCI in Work and Learning, Life and Leisure - 6th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering, USAB 2010, Klagenfurt, Austria, November 4-5, 2010. Proceedings , 2010, USAB.

[25]  Jen-Her Wu,et al.  Mobile computing acceptance factors in the healthcare industry : A structural equation model , 2006 .

[26]  Tsipi Heart,et al.  Older adults: Are they ready to adopt health-related ICT? , 2013, Int. J. Medical Informatics.

[27]  Linda Little,et al.  E-health , 2008, BCS HCI.

[28]  Gregory T. A. Kovacs,et al.  A multiparameter wearable physiologic monitoring system for space and terrestrial applications , 2005, IEEE Transactions on Information Technology in Biomedicine.

[29]  Martina Ziefle,et al.  Accounting for User Diversity in the Acceptance of Medical Assistive Technologies , 2010, eHealth.

[30]  Martina Ziefle,et al.  Smart Home Technologies: Insights into Generation-Specific Acceptance Motives , 2009, USAB.

[31]  A. Thompson,et al.  The meaning of patient involvement and participation in health care consultations: a taxonomy. , 2007, Social science & medicine.

[32]  Robert Steele,et al.  Elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare , 2009, Int. J. Medical Informatics.