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Exploring the Benefits of Using Motes to Monitor Health: An Acceptance Survey

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Exploring the Benefits of Using Motes to Monitor Health: An Acceptance Survey
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  Exploring the Benefits of Using Motes to Monitor Health: An Acceptance Survey Einstein Lubrin Elaine Lawrence Agnieszka ZmijewskaKarla Felix Navarro Gordana Culjak  Department of Computer Systems, Faculty of Information Technology University of Technology Sydney (einstein;elaine;aga;karl;gordana}@it.uts.edu.au Abstract Motes which are tiny, wireless sensor devices (Smart  Dust) have the potential for transforming the bio-medical and healthcare industry sector. Researchers consider motes as prototypes for nano-devices (built  from off-the-shelf technology also known as commodity based hardware), which will become a reality in 10  years time. The bio-medical and healthcare market is among the fastest growing markets for WiFi and other Wireless LAN Technologies. Motes are being trialed  for emergency triaging, patient profiling and monitoring and education. This paper reports on the  findings of a second anonymous web survey of over 100 participants from Australia, Europe and North  America, aimed at investigating the possible acceptance of Motes as a reliable and efficient health monitoring tool. An acceptance model, Unified Theory of Acceptance and Use of Technology (UTAUT) has been applied to determine how viable this technology will be in medical institutions and patients’ homes. This paper reports specifically on the subjective comments made by the survey participants in an effort to measure the acceptance or non-acceptance of motes  for monitoring health. Key-words  –   motes, wireless, sensor, networks, acceptance, model, healthcare 1.Introduction Identified areas of research significance in wireless applications include consumer awareness and satisfaction with mobile services, upcoming trends that may influence future adoption, and social and work  practices that may favour the adoption and use of wireless application services [9]. Our multi–method research approach has resulted in the development of three prototypes to demonstrate the viability of commodity based wireless sensor networks (Motes) for health monitoring of chronically ill or aged persons. Also we have conducted two anonymous web surveys on the potential acceptance of these devices as health monitors by users. In this paper, we report on the results of the second, anonymous web- based survey of 103 participants from Europe, Australia and North America. This study investigated the degree of awareness, future adoption and potential uptake of wireless sensor networks (WSNs) for health monitoring. The authors conducted this second survey  based on the acceptance model, TheUnified Theory of  Acceptance and Use of Technology  (UTAUT) as a follow up to their previous pilot study [7] to help  predict user acceptance or rebuttal of Wireless Sensor  Networks and their reasons. In this current study, in order to answer the research question, a qualitative analysis of the participants’ open-ended survey responses was undertaken and is reported in this paper. The statistical analysis of the quantitative data of this current survey is the subject of the next iteration of this ongoing work and is not discussed in this paper.The initial pilot survey targeted a narrow group of IT professionals and students from Australia and Canada and produced positive results (n= 59). [7]. In order to gain a more representative sample of the  population, the current follow-up survey targetted a wider and more international cohort to ascertain the acceptance of motes in healthcare environments in a number of countries. We also modified some of the questions to ensure people were not confused by the Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) © 2006   IEEE  language and also inserted text boxes to elicit more information from the anonymous participants (n = 103) from Europe, Australia, Canada and the United States. Finally we placed an open ended question at the end of the survey to allow the participants to comment on any issues or concerns they may have had with either the survey or the motes. The authors invited anonymous participation from medical personnel, health departments and health academics and students as well as from a wide range of academics from different faculties such as Business, Information Technology and Law in a number of countries including Norway, Greece, the United States, Canada and Australia. Participants were emailed the survey’s website address and asked to complete the web survey and distribute the survey to colleagues in the same industry sector. After 2 months we had 103 completed surveys. Unlike the first pilot survey [7], this one allowed for text input by the participants and this paper concentrates on the discussion of these comments with the aim of investigating the degree of awareness of this type of technology and its potential acceptance. Part two of this paper provides an overview of Motes and their significance as a wireless network technology for health monitoring and outlines the three  prototypes the research team has developed so far. The third section presents the acceptance model, Unified Theory of Acceptance and Use of Technology (UTAUT) which has been applied to determine how viable this technology will be for health monitoring in medical institutions and patients’ homes. In the fourth section the authors discuss the results of the second survey – in particular the comments made by the survey participants. Finally the paper concludes and  points the way to further research. 2.Technical Overview of Motes Motes, commonly known as “Smart Dust,” are tiny microcomputers that employ wireless media to communicate with other Motes. (See Figure 1). Made with off-the-shelf parts, they are relatively low cost compared with other wireless devices with the same functionality and allow for what is known as Commodity-Based Wireless Networking. They range in size from a few centimetres to a matter of millimetres; as a result, they can be placed in the most space-constrained area. Each Mote has the capability of providing various sensor measurements, ranging from measuring the surrounding magnetic field and sound level, to measuring temperature and acceleration. With a variety of sensor types, applications for these Motes can include military applications (enemy detection) or in health applications (monitoring of vital statistics). However, these and many other applications require another feature inherent in every Mote, namely the ability to self-form an ad-hoc wireless network with other Motes [1]. As an individual component, a Mote has limited benefits yet as part of a network, a mote becomes much more  powerful and provides more advantages as outlined in Table 1. Table 1 Advantages of Motes as networking devices Motes Advantages  Network Ability to cover a larger area Multihop Protocol Ability to forward packets from other Motes to one that is close by, but cannot be reached by the srcinal sending Mote  Network Adaptability Depending on the arrangement of the devices (either due to new Motes within range or failed Motes in the network), they can form a network that provides the best path for communication Figure 1 Mica2 Mote and Mica2Dot Mote with sensors attached Because these Motes are wireless and can self-form into a network they may be placed in remote regions which people find difficult to access. All these  benefits, however, come with a price. The Motes’ small size limits the amount of power available to each device yet the effect of this constraint is reduced since most of the component parts of a Mote consume little  power[1]. As an example, Berkeley’s Mica family of Motes uses widely available AA 4.5V batteries as their Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) © 2006   IEEE   power source. In addition to this, the individual components can be programmed to sleep when not in use. By employing a particular sleep algorithm, power savings can be optimal [1]. 3.Problem Formulation We have developed a multi-method research design comprising of a combination of research methodologies [2]. These include a literature review coupled with the development of three working  prototypes to test the viability of Motes as healthcare monitors as well as the web based surveys. Initially we undertook a wide ranging literature review on the use of Motes in various applications, in particular in the health monitoring area [4], [5] . Table 2 illustrates examples of health monitoring. Table 2 Health monitoring examples Health Monitoring Examples Monitoring of an individual’s vital statistics  body temperature, blood  pressure, etc Monitoring of an individual’s surroundings ambient lighting, room temperature, air pressure Early warning system for doctors and medical  professionals via signals sent directly to the medical professionals from the monitoring equipment Context aware applications saved settings for an identified individual The use of Motes solves many of the problems and achieves many of the goals raised by the four  points above. Current devices used for monitoring an individual’s vital statistics, though highly accurate, are  bulky and require wired connection between the individual and the monitoring device. Using Motes would reduce the amount of space required by these devices and only the Mote(s) would be connected to the patient [4],[5],[6],[8]. Monitoring an individual’s surroundings provides supplementary statistics, which may show a correlation with the patient’s immediate state. It also serves as a tool for context-aware applications; for example, room settings in a hospital can be adjusted according to the requirements and condition of the patient. The biggest advantage that health monitoring provides is the fact that it serves as an early warning system for doctors and other medical  professionals [6]. Statistics gathered from the Motes may show a trend or correlation between each type of variable that may, upon reaching a set threshold, alert the health professionals. These statistics may be accessed via a web server to authorized individuals at any moment and location, thus, making it a very flexible system. [6]. We have developed prototypes to ensure that we understood the intricacies of working with these tiny devices over a period of 18 months. Many technical issues had to be resolved as these devices are still in a  preliminary development stage and they are expensive. Our experience has been gained by working with a set of 10 motes (both Mica2 and MicroDot Motes) and sensors such as light, temperature, sound, and accelerometers rather than simulators which seem to be used by many other researchers in the wireless sensor network arena. 4.Problem Solution Our first research test bed demonstrated that the use of the network management tool, Multi Router Traffic Grapher (MRTG). MRTG enables data from the motes to be displayed graphically on the web and thus allows medical staff the ability to access patient data from anywhere in the world by the use of a simple web  browser [8]. Our second research test bed demonstrated a similar system using PDAs. We were able to show that MRTG’s compression is such that even with months of data, the amount of space required would only be a matter of hundreds of kilobytes. A remote feature of our system is also available, where authorized users are able to view the information graphically on a website. This data can be displayed on a laptop or PDA which has Internet connectivity. Our system is more easily set up than the proprietary implementations [6]. Our third prototype improves on the performance and reliability of the system by separating the ‘business logic’ from the interface. The addition of Crossbow’s Stargate allows for the ability to access the Mote sensor network by various mobile devices (PDA, cellular, etc).Descriptions of this  practical work have been written up [5],[6],[8] and we have completed a study that illustrates the development of a more intuitive user interface on a PDA to control the use of a number of motes (see Figure 2) . The authors believe such an interface is essential if users who are ill or infirm want to interact with these devices to monitor their health Our next step was to ascertain the likely level of acceptance of these devices as health monitors by carrying out two user acceptance surveys which are discussed in the following section.. Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) © 2006   IEEE  Figure 2 Prototype 3 showing PDA user interface 4.1.Unified Theory of Acceptance and Use of Technology (UTAUT) Traditionally acceptance models have been used to help explain and predict adoption of new technologies. They are based on specific factors, or constructs, that influence the individual's decision to adopt or reject a new technology. Venkatesh et al. (2003) closely examined eight acceptance and adoption theories and combined the relevant constructs from different theories under one model, the Unified Theory of Acceptance and Use of Technology (UTAUT). UTAUT includes four determinants of user acceptance and technology usage. These acceptance models shared seven major concepts according to their constructs, of which four are considered direct determinants for user acceptance namely [10]. (See Table 3 and Figure 3) • Performance expectancy • Effort expectancy • Social influence • Facilitating conditions Along with these determinants are moderators (gender, age, experience and voluntariness of use) which affect the strength of the determinants. These determinants and moderators influence an individual’s  behavioural intention (his/her planned intention towards the technology) – generally, to use it or not [10]. The authors applied the Unified Theory of Acceptance and Use of Technology (UTAUT) to  predict the potential adoption of motes technology. The researchers conducted a preliminary anonymous web survey to record the attitudes and perceptions of people from Australia and Canada[7] towards the use of motes as a health monitoring tool. The results have already  been shown to follow the UTAUT model (see Table 3); thus, the necessary ingredients for technology acceptance are present, according to results from our first pilot survey. Figure 3 Original Unified Theory of Acceptance and Use of Technology Model [10] An overview of the key four features of UTAUT and results from the First Pilot Survey are set out in Table 2 which outlines definitions of the determinants, the hypotheses and results from the first pilot survey. Table 3. Overview of first pilot survey based on UTAUT Determinants Hypothesis Results First Pilot Study Performance Expectancy: The degree to which an individual  believes that using the system will help him or her better attain significant rewards The influence of Performance Expectancy on  behavioural intention will be moderated by  gender   and age ,such that the effect will be stronger for men and  particularly for younger men[10] Results from our survey confirmed that younger men did have a more affirmative response towards the Motes’ usefulness. Effort Expectancy: the degree of ease associated with the use of the system The influence of Effort Expectancy on behavioural intention will be moderated by age and  gender  , so that the effect will be stronger in women,  particularly older women. [10] Our sample did not include a sufficient number of female  participants to validate this hypothesis. Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) © 2006   IEEE  Social Influence: the degree to which the individual  perceives that important others believe he or she should use the new system The influence of Social Influence on  behavioural intention will be moderated by  gender  , age , voluntariness  and experience, such that the effect will  be stronger for women,  particularly for older women and for those in the early stages of their experience. [10] From our results, we can validate that younger  persons who have limited experience do have stronger response to Social Influence Facilitating Conditions: the degree to which an individual  believes that an organizational and technical infrastructure exists to support use of the system Facilitating Conditions will not  have a significant effect on  behavioural intention [10] The influence of Facilitating Conditions on usage will be moderated by age and experience ,such that the effect will be stronger for older individuals,  particularly those with increasing experience [10]  Note that usage here cannot be validated as the Motes have only recently  become commercially available and the main users at  present are researchers. It only  provides validation up to an individual’s  perceived intention on usage. 4.2.The Second UTAUT Survey In this follow up survey we enhanced the questions to ensure more focussed and open ended responses than were ascertained from the first pilot. The questions were grouped in 4 Sections – Section 1 asked if the respondents were 1: familiar with motes and 2: whether they had previously completed the survey. If the respondents had not heard about motes they were able to go to a set of screens which defined Motes and  pictured them – see Figure 3. Figure 4 Brief overview of Motes Screen Section 2 dealt with queries about Healthcare and Monitoring to establish if the respondents were satisfied with the current healthcare in their countries. Section 3 consisted of a series of questions that related to the use of Motes in Health monitoring, based on the four UTAUT determinants. Section 4 was concerned with demographic questions. The aim of the survey is to further test how the moderators affect the strength of the 4 determinants as defined by UTAUT. As mentioned previously, the quantitative analysis of the results of these answers is the subject of another paper. This paper deals with the answers to the open ended question at the end of the survey when the respondents were asked to comment on any issues they had with the survey and/or the use of Motes in Health Monitoring. 4.3. Demographic Information For this survey the responses numbered 103 from Europe, Australia, Canada and the United States. The survey covered a time period of two months with the cut off date of 31 May 2005. Seventy five people who completed the second survey had not heard of Motes  before, twenty five had heard about them whilst three had completed the first survey so did not have to fill it in all again. Two of these last mentioned people reported positive reactions to the potential use of motes in the health monitoring arena. Most of the respondents were from Australia and the other countries represented are shown the table that follows.The vast majority of respondents had not heard of Motes before undertaking this survey. Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06) © 2006   IEEE
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