The use of personalised, mobile applications in preventative healthcare

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1 The use of personalised, mobile applications in preventative healthcare CHRISTINA YUM The University of Sydney General Terms: Mobile personalisation, preventative healthcare, diet and exercise monitoring 1. INTRODUCTION In Australia and throughout the world, preventable diseases place a large burden on individuals, families, communities and the healthcare systems that support them. Personalised, mobile applications have the potential to ease the strain on health systems by focusing on reducing key risk factors. Australia has seven Health Priority Areas as defined by the Australian Institute of Health and Welfare [AIHW 2008]. Within these, Cardiovascular Health and Cancer Control are dominant in terms of number of deaths and costs to the community. A majority of health problems seen in these priority areas are preventable, with major risk factors including smoking, high blood pressure, high blood cholesterol, insufficient physical activity, being overweight or obese, poor nutrition and diabetes. The prevalence of these lifestyle diseases both in Australia and globally is a critical concern, and shows no evidence of easing. The further development of personalised, mobile applications to assist individuals in improving these aspects of their lifestyle is an important, initial step in improving the health status of current and future generations. Personalisation is a central consideration in the development of preventative health applications to engage the user and deliver appropriate feedback and advice. A healthy lifestyle is achieved differently by various individuals; what is effective for one person, may have no effect on the next. To be useful, any type of intervention needs to be tailored to suit an individuals experiences, preferences, needs and goals. Mobile technology is now effectively supporting patients, healthcare professionals, administration staff and the greater population across many medical and health domains. Mobile telemedicine, patient monitoring technologies, and intelligent emergency response have made a large contribution [Varshney 2003]. The increased saturation of mobile devices, and the advancing capabilities and processing power of Smartphones has motivated recent research to consider the role that mobile applications can play in preventative healthcare. The omnipresence of mobile devices support information access and data entering at point of incidence when health related decisions are made. Broadly, preventative healthcare can cover many aspects of a person s lifestyle including nutrition, physical activity, smoking and alcohol intake amongst others. However, this paper will focus on mobile, personalised ap- SID: INFO5010 Mobile Personalisation Reading Course Draft Essay Submission

2 112 Christina Yum plications that support the promotion of nutrition and physical activity as these two determinants are significant contributors to the causes of morbidity and mortality in Australia. This paper will present the most important personalised, mobile applications that support preventative healthcare in the areas of nutrition and physical activity. Section 1 will introduce the functions of such applications, to give an understanding of how these can benefit a user. An overview of applications that promote nutrition and physical activity will then be given, highlighting their objectives and unique contributions. Section 2 will summarise the characteristics of these systems, highlighting the various approaches and giving examples of each. Section 3 will then conclude and provide a short overview of further research challenges. 2. MOBILE PREVENTATIVE HEALTHCARE SYSTEMS 2.1 Functions Preventative healthcare applications developed for mobile devices exhibit one or a combination of three main functions. Some applications are mainly used for assessment of health status for example, [Zhu et al. 2008], others promote awareness of current behaviour to persuade or motivate the user for example, [Consolvo et al. 2008] and some are focused on intervention or behaviour change for example, [Tsai et al. 2007]. These functions are delivered in various ways by current mobile applications, allowing them to replace and/or support the traditional role of professionals in preventative healthcare, encourage self-efficacy and independence in maintaining one s health status. 2.2 Promoting Nutrition Assisting individuals to achieve a nutritious diet is a major opportunity area for the use of personalised, mobile applications in preventative healthcare. Diet is a major health determinant for many lifestyle diseases. Work has been done in this area through a number of different approaches. Yaojin [2008] presents a general behaviour change application called the Wellness Diary System. The Wellness Diary System is based on Cognitive Behaviour Therapy that focuses on the importance of self-observation to achieve behavioural changes. Manual, self-reported input was required for use of this system and was collected for eight parameters - Weight, Exercise, Eating, Alcohol, Sleeping, Smoking, Stress and Steps through both a mobile and web-based interface. The user was able to set their own daily and weekly goals and the user received personalised feedback from the system based on their self-reported performance for each parameter. The data collected was not very rich; the Eating parameter only tracked Unhealthy to Healthy values allowing for little useful analysis. Health professionals were supported as another user group of this system to manage users, support intervention and for research purposes. The added support that individuals received when health professionals used this system increased its value. A messaging function allows personal communication between the user and the health professional, leading to more effective intervention than system-generated feedback alone. It can be seen that there are significant limitations with self-reported data, particularly when it comes one s diet. Both overweight and average weight people

3 The use of personalised, mobile applications in preventative healthcare 113 have been seen to under report their food intake [Poppitt et al. 1998] which can be for a variety of personal, social or cultural factors. Other mobile applications that promote nutrition are capable of collecting much richer data about the user s diet through more innovative methods. Zhu et al. [2008] report on a novel dietary assessment tool that uses a mobile device with a built-in camera to keep an accurate account of daily food intake by photographing food items before and after consumption. In this work, image analysis and visualisation tools with a nutrient database are used in an attempt to automatically identify and quantify the nutritional value of the food consumed. The user can interact with the system on a multimodal basis if this automated classification fails. The user has the option to identify the food in the picture through on-screen writing, text input, browsing through the database in a tree structure or searching the database. The image analysis system used had many limitations. Firstly, the nutritional value of many foods cannot be identified from pictures alone for example, skim milk and full cream milk are visually, nearly indistinguishable. The imaging system implemented used simple 2D images, although future work involving 3D imaging systems would improve estimation of amount of food consumed. However, it should be considered that the hardware and software required to generate such images is not standard on mobile devices unlike the current implementation, which may present a significant barrier to adoption. Other academic and commercial food-tracking systems also utilise the built-in camera on mobile devices to store images of foods consumed. These photos are then referenced by the user, but in some systems, are also directly sent to a nutritionist for professional evaluation for example, Myca Nutrition s Picture Food Journal [Myca 2007] and WellNavi [Wang et al. 2006]. Work by [Tsai et al. 2007] also identified the importance of self-monitoring in preventative healthcare and proposed a mobile application called PmEB (Patient- Centered Assessment and Counseling Mobile Energy Balance) based on Ecological Momentary Intervention (EMI) [Intille et al. 2003]. Similarly to The Wellness Diary System, PmEB was reliant on accurate user input of calorie consumption and expenditure and had both a web and a mobile client which were used for different functions. However, it was much more sophisticated in terms of the food consumption data it collected and used. The system consisted of a client application running on the user s mobile and a server application running on a web application server. This architecture allowed for reminder messages to be sent from the server to the user to update their caloric intake and expenditure, stored a food and activity database which could be referenced by the user, and kept a record of the user s data. In this way, food items could be looked up prior to consumption, to promote awareness of healthy choices and it provided an easier and more accurate means of reporting. The EMI focus of the system meant it also monitored changes and provided just-in-time, proactive and personalised information to the user as an intervention function. Other mobile systems that support both dietary and physical activity tracking features include Hyperfit [Jarvinen et al. 2008], BALANCE [Denning et al. 2009] and Virtual Specialist [Silva et al. 2006] although each handle nutrition promotion in unique ways. Hyperfit is a hybrid media application combining mobile and print technology and was aimed to mimic the process of personal nutrition counseling.

4 114 Christina Yum Similar to PmEB, it also has a web and mobile interface and uses an extensive food database to provide nutritional information and facilitate the creation of food diaries. Hyperfit is unique in that it also supports data collection from a barcode scanning application which reads 2D barcodes with a camera phone. This can lower the cognitive load associated with dietary reporting. Analysis of the user s nutrition is done by the web-client to provide personalised summaries and counseling. The BALANCE application [Denning et al. 2009] is based around a Personal Fuel Gauge which promotes awareness of a user s relative caloric intake versus caloric expenditure and also provides functions to set goals and give alerts to influence the user s behaviour. BALANCE supports two food databases, one master database with detailed nutritional information and one personalised database to store the user s previous food choices to assist subsequent access. This personalised database can also store custom entries that may not exist in the master database. Future work proposed by the authors included the use of context and historical data to streamline food entry based on the notion that certain foods are consistently eaten at particular times and locations. Finally, Virtual Specialist [Silva et al. 2006] was designed as a communication facilitator between health professionals and individuals participating in a preventative health program. The system involves a dedicated home unit; comprised of scales, a blood pressure monitor, a digital pen, a printer, speakers and CPU. The home unit prints diet and exercise plans as prepared by a health professional, which the user can then annotate with the digital pen and send back to the professional for feedback. Audio messaging is also possible with the home unit for personalised attention from the health professional. A Mobile Virtual Specialist is used on the user s mobile and accesses a web server to receive recommendations (in the form of bar charts) about various foods, as requested by the user manually, or by scanning an associated barcode with the mobile device. This approach has the potential to deliver widespread preventative healthcare, while easing the burden on health professionals. The personal attention also has the potential to sustain high levels of motivation amongst users. Future work plans to conduct a concept evaluation which would better indicate the most useful components of such a system as the entire system would be very expensive to obtain as is. It can be seen that personalised, mobile applications to promote nutrition have been developed from a range of approaches. The more sophisticated systems are based on client-server architectures enabling mobile devices to access large amounts of nutritional information, enhance storage capacity, reduce client-side processing demands and enable interaction with health professionals to ultimately provide a richer, more personalised user experience. Further work in this area is focused on streamlining dietary assessment, providing personalised intervention and enhancing nutritional awareness through context-aware, location-based applications making use of sophisticated user models. 2.3 Promoting Physical Activity Another set of preventative healthcare applications aim to assess, enhance awareness of or actively promote physical activity. As mentioned previously, BALANCE [Denning et al. 2009] combines activity inference with caloric input to provide helpful feedback and advice to the user regarding their energy needs. It uses the Mobile

5 The use of personalised, mobile applications in preventative healthcare 115 Sensing Platform [Choudhury et al. 2008] which is a small, battery powered device that incorporates 8 sensing capabilities and is worn around the user s waist. In the initial proof of concept implementation, only data from the accelerometer was used for activity recognition through a trained Naïve Bayes classifier. Validation of these measurements was done through treadmill testing and indicated an overall accuracy of 87%. The BALANCE caloric expenditure estimates were compared against the volume of oxygen uptake (V0 2 ) which is the gold standard in measuring caloric expenditure. The main downfall of the BALANCE estimates was that its data was based on activity sensing and not physiological response. Therefore, when a user stopped moving, their caloric expenditure as measured by BALANCE dropped significantly while V0 2 remained high. The tests were done at a consistent pace on a treadmill so it could be assumed that the accuracy of this system would drop significantly when estimating the caloric expenditure of inteval or cross training. Future work aims to develop an inference engine that can recognise when a user is performing particular gross motor patters such as sitting, walking, running or bicycling using data from multiple sensors. As an example, an accelerometer and GPS receiver could be used to infer a user s method of transportation and account for that in caloric expenditure calculations. Hyperfit s [Jarvinen et al. 2008] method of collecting energy expenditure data is less sophisticated than BALANCE. The mobile component of this system allows for recording of physical activity through manual input, and can also be added through a heart rate monitor. Calculators, self-tests and daily or weekly summaries for weight management were also included in this system. It can be seen that Hyperfit pays less attention to the opportunities of personalised, mobile technology to promoting physical activity, with greater innovation in their approach to tracking and changing diet behaviour. A virtual Personal Trainer metaphor is used to deliver feedback and encouragement to the user, and results from a user study indicate there is value added to the service through the use of a mobile device. Hyperfit has been commercialised, as has previous work done by the authors around barcode reading using cameras on mobile phones. The Hyperfit application may have only been designed as a spin off product from their successful previous work. The commercialisation intent is obvious throughout the description of the work with business model development being part of the research approach, and willingness to pay used as a measure in the user study. The Mobile Fitness Companion [Ståhl et al. 2008] and MOPET, a Mobile Personal Trainer as described by Buttussi and Chittaro [2008] are mobile applications that also provide personalised feedback and training support through a virtual Personal Trainer. The Mobile Fitness Companion has a home unit and a mobile component which accompanies the user when exercising and communicates with the home unit over the regular mobile network. The Companion is able to download a pre-determined plan from the home unit and suggest planned exercise to the user, if this exercise is rejected, the Companion asks the user to suggest an alternative. In the current implementation the Companion takes the physical form of a rabbit to elicit information about the type of exercise the user is doing. The system uses a GPS receiver to obtain the user s position, collect distance and speed data and estimate calories burnt. The Mobile Fitness Companion is mainly driven

6 116 Christina Yum by speech input and output to interact with the virtual trainer. Push to talk is used to activate speech recognition software which is then responded to through real-time, system generated speech output. The Mobile Fitness Companion is similar to MOPET in that it displays a 3D embodied agent and uses audio output to inform and motivate the user, although audio is only used as output and there is no real dialogue between the user and the system. However, MOPET is a sophisticated, context-aware and user-adaptive wearable system that can for example, suggest stretching and strengthening exercises based on the users current condition or location and demonstrate these with interactive 3D animations. It incorporates a heart rate monitor and 3D accelerometer worn around the the users chest, and a mobile device with built-in GPS allowing heart rate, position and exercise time to be analysed and visualised by the system. MOPET supports sophisticated personalisation through the development of user models. The initial user model is built on first run of the system, with the agent requesting the users gender, age, weight and height. An autotest is then initiated, in which the user must walk onto and off a step to provide baseline physiological data. This initial user model is then used to suggest the user increase, or decrease exercise intensity, provide advice and propose exercises. The user s behaviour in response to these is then used to further develop the user model, including experience and preference of exercises. Future work in this area is to support goal setting for specific fitness components, for example, muscular conditioning or flexibility and to develop a exercise recommender system combining the user s needs and their exercise preferences. MPTrain [Oliver and Flores-Mangas 2006] and TripleBeat [de Oliveira and Oliver 2008] are two mobile preventative healthcare applications which draw on the positive effect of music to promote physical activity. MPTrain is the predecessor of TripleBeat, and was the first system to directly exploit the effect of music on physiology and physical activity in a real-time, mobile manner. Other systems deliver music in a one way manner, and although may independently monitor heart rate, they do not provide personalised feedback about the users state or performance to affect music selection. The system used physiological sensors including a 2D accelerometer and an electrocardiogram (ECG) wirelessly connected to a small personal computer worn by the user when walking, jogging or running. The user inputs physiological or time/distance goals and the system selects and plays music based on beat and average energy parameters to guide the user to achieving their goals. It uses an algorithm that maps between musical features, exercise levels and desired physiological response, encouraging the user to slow down, speed up or maintain pace. This system also maintains user models incorporating physiological data and their personalised music library. Enrichment of the user model is planned for future development to take into account the user s past performance and specific physiological responses to each song. TripleBeat builds on the MPTrain system by incorporating musical feedback and a virtual competition for the user to provide real-time feedback to the user both audibly and visually. A glanceable display is used in TripleBeat to present easy to understand, physiological and pace data to the user on a mobile device with low cognitive effort. Additionally, the display provides information about the competition, including the user s current position

7 The use of personalised, mobile applications in preventative healthcare 117 and distance between (virtual) opponents. Another unique mobile system which uses a glanceable display to increase awareness of physical activity levels is UbiFit [Consolvo et al. 2008]. Where TripleBeat utilised a glanceable display to show qualitative information to motivate the user during exercise, UbiFit s display promotes awareness of the quantity of physical activity achieved in a weekly period. As used in BALANCE [Denning et al. 2009], UbiFit utilises the Mobile Sensing Platform [Choudhury et al. 2008], but the system only uses data from the accelerometer and the barometer to infer walking, running, cycling, using an elliptical trainer and using a stair machine. UbiFit makes enhances awareness of physical activity levels through a stylised, glanceable display of a garden on the user s mobile. Frequent and varied exercise is promoted, with different flowers representing physical activity detected by the user s Mobile Sensing Platform and butterflies signifying goal achievement. The display was well received; participants who used it in a user study maintained their level of activity over time and on holiday weeks, whereas the physical activity of participants who did not have the display dropped significantly. Despite this positive result, there was no reported weight loss amongst the participants and the usability and accuracy of the Mobile Sensing Platform troubled many participants. There are also a number of other systems which use pedometer readings to promote increased physical activity. Including Fish n Steps [Lin et al. 2006], Houston [Consolvo et al. 2006] and Chick Clique [Toscos et al. 2006]. Fish n Steps [Lin et al. 2006] utilises a glanceable display of a virtual fish tank, similar to UbiFit but on a desktop. Both Houston and Chick Clique, introduce a social dimension to promote awareness of physical activity levels and encourage behaviour change. Both of these systems allow small groups of friends to use their mobile devices to set goals and share step counts with their friends. Although self-reported step counts are not accurate indicators of an individual s nutrition or overall energy expenditure, a sense of healthy competition is important to encourage physical activity and develop health responsibility amongst groups. The benefits of social networking in preventative healthcare is also seen in work by [Sohn and Lee 2007] in their development of UP Health - an instant messaging system that shares context information relating to various health aspects to support social interaction. Although this system is used from the desktop, it could complement use of personalised, mobile applications to give the user a sense of perspective and facilitate the sharing of information and experiences to benefit the wider population. 3. CHARACTERISTICS OF PREVENTATIVE HEALTHCARE APPLICATIONS As described above, there are a large number of mobile, personalised applications providing preventative healthcare through the promotion of a nutritious diet and regular physical activity. While some applications focus on lifestyle assessment, or collecting food intake and exercise information through a mobile device others incorporate system components to provide personalised feedback and enhance user awareness of their lifestyle behaviours. This body of work can be further analysed by comparing the characteristics of these applications, including system components, the involvement of health professionals, motivational elements and personalisation.

8 118 Christina Yum 3.1 System Components The majority of feature-rich mobile applications use additional system components to augment the capabilities of the mobile device. This commonly included a webclient used in some cases to personalise the system [Tsai et al. 2007], in others to access detailed summaries and visualisations of data collected by the mobile [Jarvinen et al. 2008] or liaising directly with a health professional [Yaojin 2008; Silva et al. 2006]. Some of the systems relied on manual input of data by the user [Yaojin 2008; Tsai et al. 2007], whereas others supported product barcode identification using a camera phone [Jarvinen et al. 2008], image processing [Zhu et al. 2008], speech recognition [Ståhl et al. 2008], physiological sensors such as heart rate monitors [Buttussi and Chittaro 2008], accelerometers and barometers on the Mobile Sensing Platform [Denning et al. 2009; Consolvo et al. 2008], built in GPS units [Ståhl et al. 2008; Buttussi and Chittaro 2008] or pedometers [Lin et al. 2006; Toscos et al. 2006] to automate data collection in varying degrees. 3.2 Involvement of Health Professionals The role of health professionals is varied amongst the existing systems. Applications encouraging awareness of nutrition and physical activity levels were generally seen to have no involvement from health professionals for example, UbiFit and BAL- ANCE and promote independent behaviour changes within users. Some systems are used as a communication tool between user s and professionals to encourage motivation through personalised feedback and attention. This has been achieved through a messaging function as seen in the Wellness Diary System, or through the delivery of diet and exercise plans compiled by a professional such as in Virtual Specialist. Other systems such as Myca Nutrition s Picture Food Journal sends images of the user s meals straight to a nutritionist for analysis. In other cases, the system aims to completely replace the role of health professionals through the embodiment of virtual personal trainers and nutritional experts as in MOPET and Hyperfit. 3.3 Motivational Elements A number of motivational elements have been integrated into various systems to keep the user engaged and further encourage preventative healthcare. Innovative approaches to motivational elements have been observed in many mobile applications. Glaceable displays allow the user to quickly grasp their progress and encourage awareness and activity to promote a healthy lifestyle. Such displays have been used in the TripleBeat application to display physiological information and progress during exercise. Using UbiFit, the user s level of physical activity, and goals for a week-long period is represented by flowers and butterflies in a garden that replaces the wallpaper of a mobile device. Visualisations of data are used in various systems to provide personalised feedback to the user on their progress. This allows the users themselves or a health professional to assess their current behaviours and suggest method for intervention. The BALANCE system uses a Personal Fuel Gauge to display the users caloric intake relative to expenditure, which also acts as a glanceable display. Other systems present food and exercise recommendations through bar graphs such as in PmEB.

9 The use of personalised, mobile applications in preventative healthcare 119 Goal setting is another key motivational element that is used throughout many preventative healthcare applications for motivation and to give the user a sense of achievement. In the Wellness Diary System, the user sets goals for each health parameter and the system automatically generates feedback on their performance. Music is seen as a major motivating element for some applications promoting physical activity. TripleBeat and MPTrain, require the user to set physiological or distance/time based goals and the system then assists them through selecting and playing specific songs. For example, when a user is slowing down, the system plays an upbeat song in the hope of motivating the user to speed up. Competition and social motivators have also been exploited in mobile preventative healthcare applications. The TripleBeat system engages the user in a virtual race, against named virtual competitors to encourage motivation. Houston, Chick Clique and UPHealth use social networking as a base for comparative analysis and knowledge sharing amongst users. 3.4 Personalisation For a preventative healthcare application to be useful and relevant to the user on an ongoing basis, some level of personalisation is critical. The before mentioned systems address this personalisation need in a number of ways. As mentioned above, goal setting is a key feature in many systems which allows for personalised results tracking and feedback to be delivered to the user. Other personalisation opportunities exist in the delivery of reminders, notifications, feedback, motivation and advice for example, TripleBeat, Hyperfit and PmEB. The delivery of personalised nutrition and physical activity plans was achieved through the Virtual Specialist system. As well as personalisation in system output, other systems utilise user models and personal data to influence calculations and streamline data input. Initial use of the PmEB system required a web-based setup task to personalise and automate the caloric balance calculations. Resting Metabolic Rate and Calorie Expenditure, which are key values in the system are determined by a number of personal and physiological factors unique to each individual making personalisation critical for the overall accuracy of the system. Useability is also enhanced through personalisation of PmEB. In the initial set-up, users are also asked to specify locations where they may be physically active, and what physical activities they might perform at each location. For food consumption, typical foods eaten for each meal are elicited to create personalised cheat sheets. These exercise and food preferences are then readily accessible from the mobile client, to avoid tedious searching among the database for commonly consumed foods, or exercise routines. Favourites can also be defined in the Hyperfit system, to streamline data input. 4. CONCLUSION In conclusion, there exists many mobile, personalised applications with the intent to promote nutrition and physical activity as a function of preventative healthcare. These systems cover three main functions; encouraging awareness of lifestyle behaviours, providing the means to assess these behaviours and facilitate intervention if these behaviours put the individual at risk of disease. A number of characteristics of such systems have been explained to classify these systems based on

10 120 Christina Yum their components, the role of health professionals, motivational elements and level of personalisation. Some areas of future work have been identified, and ongoing research challenges including increasing user adoption and trust, improved system accuracy and the development of immature technology and the privacy and security of data will add value to these systems. Great innovation and potential can be seen however, in the initial development of personalised, mobile applications to improve the health status of individuals, while promoting independence and encouraging responsibility to reduce the burden of preventable diseases in the future. REFERENCES AIHW Australia s health Canberra: Australian Institute of Health and Welfare. Buttussi, F. and Chittaro, L MOPET: A context-aware and user-adaptive wearable system for fitness training. Artificial Intelligence in Medicine 42, 2, 153. Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., Hightower, J., et al The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, Consolvo, S., Everitt, K., Smith, I., and Landay, J Design requirements for technologies that encourage physical activity. In Proceedings of the SIGCHI conference on Human Factors in computing systems. ACM New York, NY, USA, Consolvo, S., Klasnja, P., McDonald, D. W., Avrahami, D., Froehlich, J., LeGrand, L., Libby, R., Mosher, K., and Landay, J. A Flowers or a robot army?: encouraging awareness & activity with personal, mobile displays. In UbiComp 08: Proceedings of the 10th international conference on Ubiquitous computing. ACM, New York, NY, USA, de Oliveira, R. and Oliver, N Triplebeat: enhancing exercise performance with persuasion. In MobileHCI 08: Proceedings of the 10th international conference on Human computer interaction with mobile devices and services. ACM, New York, NY, USA, Denning, T., Andrew, A., Chaudhri, R., Hartung, C., Lester, J., Borriello, G., and Duncan, G Balance: towards a usable pervasive wellness application with accurate activity inference. In HotMobile 09: Proceedings of the 10th workshop on Mobile Computing Systems and Applications. ACM, New York, NY, USA, 1 6. Intille, S., Kukla, C., Farzanfar, R., and Bakr, W Just-in-time technology to encourage incremental, dietary behavior change. In AMIA... Annual Symposium proceedings [electronic resource]. Vol American Medical Informatics Association, 874. Jarvinen, P., Jarvinen, T., Lahteenmaki, L., and Sodergard, C Hyperfit: Hybrid media in personal nutrition and exercise management. Pervasive Computing Technologies for Healthcare, PervasiveHealth Second International Conference on, Lin, J., Mamykina, L., Lindtner, S., Delajoux, G., and Strub, H Fish n Steps: encouraging physical activity with an interactive computer game. Lecture Notes in Computer Science 4206, 261. Myca Myca nutrition. Oliver, N. and Flores-Mangas, F Mptrain: a mobile, music and physiology-based personal trainer. In MobileHCI 06: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services. ACM, New York, NY, USA, Poppitt, S., Swann, D., Black, A., and Prentice, A Assessment of selective underreporting of food intake by both obese and non-obese women in a metabolic facility. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity (USA). Silva, J. M., Zamarripa, S., Moran, E. B., Tentori, M., and Galicia, L Promoting a healthy lifestyle through a virtual specialist solution. In CHI 06: CHI 06 extended abstracts on Human factors in computing systems. ACM, New York, NY, USA, Sohn, M. and Lee, J UP health: ubiquitously persuasive health promotion with an instant messaging system.

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