Mobile health monitoring services have the potential to provide individual health profiles and
monitor patients’ health conditions. While there is a variety of systems and devices to help
patient’s health management, there is little understanding regarding the factors that influence
mobile health monitoring service perceptions. Thus, this UK study will examine the factors
that influence users across different age groups towards well-being and Mobile Health
monitoring services. This study applied a conceptual framework based on the Unified Theory
of Acceptance and Use of Technology (UTAUT) model to examine the factors that may
influence the attitude towards mobile health services among consumers

De Coster, R

Sabri Tekantape, E

Brunel University Research Archive

2018 BAM Doctoral Symposium  Consumer Survey into Factors  Influencing Mobile Health Monitoring Service perceptions in the UK  By Elahe Sabri Tekantape E-mail: Elahe.SabriTekantape@brunel.ac.uk  Tell: +44 (0) 1895 265784 Dr Rebecca De Coster E-mail: Rebecca.decoster@brunel.ac.uk  Tell: +44 (0) 1895 265784  College of Engineering, Design and Physical Sciences  Brunel University London, UK Management Discipline: Innovation   Keywords:  Mobile Health Monitoring Services, Technology Acceptance, and Wellbeing  Brief Statement of the Research: This study will draw nine variables from Mobile Health monitoring services studies, and the unified theory of acceptance of technology model (UTAUT model)   1  2018 BAM Doctoral Symposium Consumer Survey into Factors Influencing Mobile Health Monitoring Service Perceptions in the UK  Abstract: Mobile health monitoring services have the potential to provide individual health profiles and monitor patients’ health conditions. While there is a variety of systems and devices to help patient’s health management, there is little understanding regarding the factors that influence mobile health monitoring service perceptions. Thus, this UK study will examine the factors that influence users across different age groups towards well-being and Mobile Health monitoring services. This study applied a conceptual framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine the factors that may influence the attitude towards mobile health services among consumers.         Keywords: Mobile Health Monitoring Systems, UTAUT Model, Well-being, Individual’s Health Profiles 2  2018 BAM Doctoral Symposium Introduction  Wearable health monitoring systems have recently attracted interest between the research community and also industry.[1,2] A variety of systems and devices have been produced and developed for providing real-time feedback information about individual’s health status, either to the user himself or health providers.[2] Mobile health monitoring services could also be highly effective for those who want to manage their health and avoid illness, especially for patients with chronic conditions.[3,4]1]Towards this, mobile health monitoring services comprise a combination of medical sensors, communication technologies and mobile computing for healthcare.[1,5] Healthcare applications divide into different categories such as: Prevention, check-ups, short-term and long-term monitoring systems, healthcare maintenance, incidence detection or fall detection, emergency intervention, treatment and also transportation. Mobile health technologies can store a significant amount of data and information on a mobile device and can update them as necessary to allow critical information on a user’s mobile device.[1]  In the UK, there are drivers towards monitoring a patient’s health condition while they are at home because of the high cost of healthcare and also the population is aging. Smartphones can provide access to individual’s health information at any time and any context. While there are many types of research that focus on the technical aspects of m-Health (Mobile Health) applications, still little research on the effectiveness of apps and the significant factors for accepting them is available.[6,7]   Purpose of the Research:  To examine the factors that influence users towards well-being and mobile health services perceptions in the UK. 3  2018 BAM Doctoral Symposium Research Questions:  1. What are the factors influencing the acceptance of Mobile Health monitoring service perceptions in the UK? 2. To what extent does social cohesion influence Mobile Health service acceptance?  3. How does the usage of Mobile Health monitoring systems correlate with well-being? 4. What are the effects of individual health profiles across the different age groups?  Theoretical Background  A. Patient’s Perception in Mobile Health Monitoring  Services Acceptance  A literature review was conducted related to Mobile health, individual’s health profile and well-being to identify the factors that influence the user’s perceptions towards mobile health technologies, and well-being. Through the literature review, the study found three perceptions: accessibility, mobility and personalisation. According to the findings in the literature review, telemedicine services can improve and help them to improve their health conditions, and it also would provide health profiles and healthcare unbound by time and space.[4] Also, according to the literature review, the factor personalisation can be beneficial for developing mobile health monitoring systems.[8,9] Personalisation is the process of producing, generating and processing the right content in the right and suitable format to an individual at the right location, and at the right time.[8] Mobile personalisation has adopted widely, and its benefits have been established. It requires specific technology, such as collaborative technology, data mining, location detection, and pattern recognition. Individuals carry the phones with them at all times, and everywhere, which can allow firms to analyse, and collect data easily and efficiently.[8]  4  2018 BAM Doctoral Symposium B. Conceptual Framework  With the rapid development of technology, especially communication and information technologies, and its integration into user’s life, accepting or rejecting the new technologies by users remains an open question.[10] Technology acceptance is about exploring the features of Mobile Health monitoring services and their relationships with the acceptance by its users,[11,12,13]  Technology adoption has been studied and examined frequently over the years. Some of the studies focus on relationships between technology adoption and the variables influencing it such as UTAUT and TAM (technology acceptance model). The UTAUT model was tested and applied to different technologies, such as online bulletin boards, and instant messengers.[14] The UTAUT model factors comprise four essential constructs: 1) performance expectation, 2) social influence, 3) effort expectancy, 4) facilitating condition. The UTAUT model has various advantages that can be beneficial for this study. The UTAUT model was developed by multiple robust models that determined that this model is exceptionally fundamental and robust. Also, the UTAUT model is 69% of indentation to use IT, which is slightly higher than other theoretical models. Last but not least, the UTAUT model frequently has been used in the healthcare research fields.[15,16]  This study adopted four factors from the unified theory of acceptance and the use of technology model proposed by Venkatesh et al.[17] as this model can provide understanding of user’s technology acceptance.[4]    5  2018 BAM Doctoral Symposium Research Methods Using mobile and wearable health information technologies (m-Health) can reduce the cost of health care and improve health results like well-being and health profile. M-Health technologies can support new methods for collecting various health data and also the outcomes of interventions. M-Health monitoring service can monitor phenomena with higher precision, improved sampling frequency, providing individual’s health profile with fewer missing data at a lower cost than traditional methods.[6] According to Creswell [18], there are three research methods which are known as qualitative, quantitative and mixed methods. ”Quantitative methodologies are used to address research questions about causality, generalisability, or magnitude of effects” however, “Qualitative methodologies apply to research questions to explore why or how a phenomenon occurs, to develop a theory, or to describe the nature of an individual’s experience”. Moreover, according to Creswell [18], mixed methods use qualitative and quantitative methods and draw upon both ways strengths and also provides an innovative approach to solve contemporary issues. After considering different research methods, qualitative and quantitative methods will be used where the quantitative method will be dominated, as there is a large number of respondents for this study that needs a large amount of time, manpower and finances in case of using qualitative methods. Moreover, the existing literature review in mobile health field shows that quantitative studies are needed in this area.[19] During the survey there will be opportunities for respondents to make comments on the issues. Following data analysis, it is anticipated that key findings will be explored further in a qualitative manner.   6  2018 BAM Doctoral Symposium Sample and Data Collection  Data for the pilot study will be collected from people in West London who use leisure facilities such as sport centres in different locations after receiving the research approval form Brunel University Research Ethics department. Questionnaires will be distributed to examine the proposed factors in the conceptual framework with the target of 50 to 60 respondents. The survey comprises a questionnaire with seven-point Likert scales. (Quantitative approach). 1= strongly disagree, 2= disagree, 3= slightly disagree, 4= uncertain, 5= slightly agree, 6= agree and 7= strongly agree.  After statistical analysis which will be done by SPSS software, the questionnaire will be revised for the main survey, which will target 200 respondents using Cluster sampling across different age groups. >>> INSERT TABLE 1           7  2018 BAM Doctoral Symposium References  [1] Lai CC, Lee RG, Hsiao CC, Liu HS, Chen CC. A H-QoS-demand personalized home physiological monitoring system over a wireless multi-hop relay network for mobile home healthcare applications. Journal of Network and Computer Applications. 2009 Nov 1;32(6):1229-41. [2] Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2010 Jan;40(1):1-2.  [3] Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task?. Translational behavioral medicine. 2011 Feb 24;1(1):53-71.   [4] Lee J, Rho MJ. Perception of influencing factors on acceptance of mobile health monitoring service: a comparison between users and non-users. Healthcare informatics research. 2013 Sep 1;19(3):167-76.  [5] Nundy S, Dick JJ, Goddu AP, Hogan P, Lu CY, Solomon MC, Bussie A, Chin MH, Peek ME. Using mobile health to support the chronic care model: developing an institutional initiative. International journal of telemedicine and applications. 2012 Jan 1;2012:18. [6] Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth and uHealth. 2015 Jan;3(1). 8  2018 BAM Doctoral Symposium [7] Lorenz A, Oppermann R. Mobile health monitoring for the elderly: Designing for diversity. Pervasive and Mobile Computing. 2009 Oct 1;5(5):478-95.  [8] Ho SY. The effects of location personalization on individuals' intention to use mobile services. Decision Support Systems. 2012 Nov 1;53(4):802-12.  [9] Saeed K. Understanding the Adoption of Mobile Banking Services: An Empirical Assessment. InAMCIS 2011. [10] Marangunić N, Granić A. Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society. 2015 Mar 1;14(1):81-95.  [11] Pai FY, Huang KI. Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change. 2011 May 1;78(4):650-60.  [12] Chau PY, Hu PJ. Information technology acceptance by individual professionals: A model comparison approach. Decision sciences. 2001 Dec 1;32(4):699-719.  [13] McCreadie C, Tinker A. The acceptability of assistive technology to older people. Ageing & Society. 2005 Jan;25(1):91-110.  [14] Im I, Hong S, Kang MS. An international comparison of technology adoption: Testing the UTAUT model. Information & management. 2011 Jan 1;48(1):1-8.  [15] Chang IC, Hwang HG, Hung WF, Li YC. Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications. 2007 Aug 1;33(2):296-303. [16] Puuronen S, Vasilyeva E, Pechenizkiy M, Tesanovic A. A holistic framework for understanding acceptance of Remote Patient Management (RPM) systems by non-9  2018 BAM Doctoral Symposium professional users. InComputer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on 2010 Oct 12 (pp. 426-431). IEEE. [17] Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly. 2003 Sep 1:425-78.  [18]: Creswell JW, Creswell JD. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications; 2017 Dec 12. [19] : Silva BM, Rodrigues JJ, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: A review of current state in 2015. Journal of biomedical informatics. 2015 Aug 1;56:265-72.  [20] Rho MJ, young Choi I, Lee J. Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International journal of medical informatics. 2014 Aug 1;83(8):559-71.  [21] Martínez A, Everss E, Rojo-Álvarez JL, Figal DP, García-Alberola A. A systematic review of the literature on home monitoring for patients with heart failure. Journal of telemedicine and telecare. 2006 Jul 1;12(5):234-41. [22] Chau PY, Hu PJ. Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of management information systems. 2002 Mar 1;18(4):191-229.  [23] Bakken S, Grullon-Figueroa L, Izquierdo R, Lee NJ, Morin P, Palmas W, Teresi J, Weinstock RS, Shea S, Starren J. Development, validation, and use of English and Spanish versions of the telemedicine satisfaction and usefulness questionnaire. Journal of the American Medical Informatics Association. 2006 Nov 1;13(6):660-7. 10  2018 BAM Doctoral Symposium  [24] Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly. 2012 Mar 1:157-78.  [25] Popescu D, Popescu S, Bodi S. A conceptual framework concerning education as factor of elders’ acceptance for smart assistive technologies. InBalkan Region Conference on Engineering and Business Education 2017 Dec 20 (Vol. 3, No. 1, pp. 275-283). De Gruyter Open.  [26] Whitten P, Doolittle G, Mackert M. Providers' acceptance of telehospice. Journal of palliative medicine. 2005 Aug 1;8(4):730-5.  [27] Taylor P. Evaluating telemedicine systems and services. Journal of telemedicine and telecare. 2005 Jun 1;11(4):167-77.  [28] Saeed K. Understanding the Adoption of Mobile Banking Services: An Empirical Assessment. InAMCIS 2011. [29]Moran S, Nishida T, Nakata K. Comparing British and Japanese perceptions of a wearable ubiquitous monitoring device. IEEE Technology and Society Magazine. 2013;32(4):45-9. [30]  Kim C, Mirusmonov M, Lee I. An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior. 2010 May 1;26(3):310-22    11  2018 BAM Doctoral Symposium Table 1. Factors deployed in the preliminary framework Factor  Definition  Sources Performance Expectancy  ‘’The degree to which an individual believes that using the Mobile Health monitoring service will help him/her to improve his/her health condition’’ [20,21,22,23,24,25] Effort Expectancy  ‘’The degree of ease that patients associate with the use of mobile health monitoring service’’  [20,21,22,23,24,25,26,27] Social Influence  ‘’The degree to which an individual perceives that important others believe he or she should use the mobile health monitoring service’’ [20,24,25,27,28] Facilitating Conditions  ‘’The degree to which an individual believes that an organisational and technical exists to support the use of the mobile health monitoring service’’ [20,25,27,28,29] Mobility ’The factor which provides a pervasive and ubiquitous connection in encouraging users’ behavioural intention to use the services” [8,26,30] Accessibility  ‘’The degree to which an individual is provided health records or health care unbound by time and space’’ [21,26] personalisation  ‘’The ability to customise m-Health monitoring services to fit the user’s need’’ [8,28] Accessibility  ‘’The degree to which an individual believes that the use of m-Health monitoring service would improve their health conditions and supply accurate and up-to-date information on an individual’’ [20,28]   12  2018 BAM Doctoral Symposium   Figure 1. The preliminary conceptual framework of m-Health monitoring service      Performance expectancyEffort ExpectancySocial InfluenceFacilitating ConditionsMobilityAccessibilityPersonalisationSocial SupportSocial Activity LevelHealth LevelMobile Health Monitoring ServiceMobile Health Monitoring Service OutcomesTechnology AcceptanceWellbeingIndividual Health Profile13  

Consumer Survey into Factors Influencing Mobile Health Monitoring Service perceptions in the UK

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Consumer Survey into Factors Influencing Mobile Health Monitoring Service perceptions in the UK

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