Going Beyond:: Challenges for Using mHealth ...

3 Going Beyond: Challenges for Using mHealth Applications for Preventive Medicine Joerg Brunstein, Angela Brunstein, and Michael K. Martin CONTENTS 3.1 Introduction........................................................................................................................... 37 3.1.1 Different Users with Different Needs as a Call for a Framework..................... 38 3.1.2 Data Flooding as a Call for Modeling.................................................................... 38 3.1.3 Guidance Desert as a Call for Intelligent Tutoring.............................................. 40 3.2 Intelligent Solutions.............................................................................................................. 41 3.2.1 Theoretical Framework on Behavior Change: Health Behavior Theories as Architecture.......................................................................................................... 41 3.2.2 Modeling Health Behavior Change and Physiological Implications for Intelligent Guidance...........................................................................................43 3.2.3 Adaptive Visualization and Simulation for Providing Intelligent Guidance for Health Behavior Change..................................................................43 3.3 Conclusions............................................................................................................................ 45 3.4 Future Directions.................................................................................................................. 46 Questions......................................................................................................................................... 46 References........................................................................................................................................ 47

3.1 Introduction In 2008, Patrick and colleagues provided an optimistic outlook for the potential of mobile phone technology “… to impact delivery of healthcare services and promotion of personal health” (Patrick 2008, p. 181). Four years later, Collins repeats stressing the potential of “… mobile devices to become powerful medical tools” (Collins 2012, p. 16). While the numbers of sold smartphones, available sensors, and mobile health and fitness applications have skyrocketed, truly intelligent mHealth applications for a majority of citizens at risk for noncommunicable diseases (NCDs) are still hard to find. In this chapter, we describe current challenges for future mHealth applications in preventive medicine posed by type of users who are neither athletes nor patients, by increasing amounts of available sensor data, and by currently missing guidance for health behavior change. For mastering these challenges, we propose a modular framework with theory, modeling, and intelligent tutoring as practiced in cognitive science. This approach clearly discriminates between theoretical knowledge acquired in the field and specific 37

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mobile applications designed to foster behavior change. That split allows reducing effort and costs. There is a need for modeling to bridge the theory–application gap. Without modeling, theories tend to be too unspecific to be implemented in applications. With models implementing the theory for specific tasks, there is a structured translation between theory and applications that allows transfer of knowledge in both directions.

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3.1.1 Different Users with Different Needs as a Call for a Framework Current mHealth applications cover a variety of services for management of patients (e.g., Tomlinson et al. 2013) with most evidence coming from management of chronic conditions programs (de Jongh et al. 2012; Fiordelli et al. 2013). Another strong domain on the market related to mHealth is advanced mobile sport applications under the Health & Fitness category of iTunes and android app stores. These programs allow athletes to track performance, similar to monitoring clinical conditions, and to plan workout, similar to management of chronic conditions. Obviously, both kinds of applications aim for different users, fulfill different functions, and serve a different purpose. In between these two extremes of functioning support, huge numbers of mobile health and fitness applications serve the majority of citizens who are neither athletes nor patients and try to lose weight or aim to become a little more physically active for preventing chronic illness. This segment of users is expected to grow significantly over the next decades given the current obesity and diabetes epidemic (WHO 2010), aging societies in developed and developing countries (WHO 2012), and huge prevalence of NCDs worldwide (WHO 2010). These users are not patients with chronic conditions yet. They have not had a heart attack yet, but they might be at risk for cardiovascular diseases. This makes a huge difference in motivation: patients after heart attack are likely to be motivated to manage their condition well to prevent the next heart attack based on their own experience. People at risk for cardiovascular diseases fight a potential heart attack that might or might not come in a few years or decades time. For those, it is quite rational to choose immediate benefits from a sedative lifestyle for sure over only potential hazards in the far future. According to a McKinsey Retail Healthcare Consumer Survey (Dixon-Fyle et al. 2012), 57% of obese participants overestimate their health status as excellent or very good. In addition, majority of U.S. adults have insufficient health literacy with more than half finding it difficult to follow directions on a prescription drug label (White and Dillow 2005). These biases impact people willingness to engage in health behaviors. People targeted by preventive medicine are not athletes either. They are often not as young and skilled as users targeted by health and fitness applications. And they might come with clinical conditions that impact their exercise program. They have a longer learning history of not living healthy than athletes. Their age and social and work environment might make it hard to exercise or eat healthy. For older adults at risk, there might also be technological barriers (Parker et al. 2013): they might not possess a smartphone or know how to operate one. To summarize, people at risk for disease or disability differ significantly from managed patients with chronic conditions and from athletes in terms of motivation, skills, habits, and clinical conditions. When providing mobile health and fitness applications, these issues need to be addressed for being useful for this group of users who will be soon the majority of citizens for many societies. 3.1.2 Data Flooding as a Call for Modeling Both mHealth applications for the management of chronic conditions and mobile sports applications track users’ performance and physiological states for monitoring and


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progress analysis. Similarly, many health and fitness applications count steps, consumed and burned calories, etc. This is matched by the highly active and highly productive technological research and development, for example, in the area of the Internet of things (e.g., Reina et al. 2013; Rodriguez-Molina et al. 2013) or body sensor networks (e.g., Lai et al. 2013). Based on its market analysis, PRWEB (2013) predicts an increase of 11% of microelectromechanical systems between 2013 and 2018. In tendency, physiological sensors become cheaper, more accurate, more convenient, better integrated in existing infrastructure, and smarter for analyzing users’ health status (e.g., Sarasohn-Kahn 2013). For example, iRhythm Technology offers a small Band Aid–like sensor that records cardio data and transmits data wireless to a smartphone. Other sensors are integrated in shoes, wristwatches, shirts, sports bras, or smartphones. Users welcome this development. According to a survey of the Pew Research Center (Fox and Duggan 2012), 85% of U.S. adults own a cell phone and half of those own smartphones. According to Gartner’s market analysis (van der Meulen and Rivera 2013), the second quarter of 2013 has seen an increase of 47% in smartphone sales. For the first time, sale of smartphones was bigger than sale of feature phones with 225 million sold smartphones worldwide during last year. The Pew survey (Fox and Duggan 2012) adds that 31% of cell phone owners have used their phone for gathering health information. Eighty-two percent of people surveyed in the United States and United Kingdom would be willing to pay for health and fitness sensors that sync with their smartphones and would spend up to $140 or £90 (Arrowsmith 2013, IHS Inc.). These data paint an optimistic outlook for serving high numbers of customers with reliable, health-relevant information. Potentially sensor data can be used for monitoring users’ existing health conditions and for warning them or their health-care provider in case they reach predefined thresholds. They could be also used for developing intelligent mobile health and fitness applications that learn from the users and accordingly adapt their support to users’ needs and preferences. At the same time, an increase in the number of users with increasing numbers of health conditions to be monitored multiplied by the increasing number of sensors will result in an exponential growth of the generated data. This holds especially for continuously monitored variables, like heart rate or blood oxygen levels. In critical care monitoring, up to 90% of alarms are false positives (Imhoff and Kuhls 2006). Comparable numbers for preventive care would overtax health-care providers’ capacities to respond to patients’ needs given the expected data tsunami (e.g., Richardson and Reid 2003, for pain management; SarasohnKahn 2013). In OECD states, there are on average 3 physicians per 1000 patients (OECD Health Data 2012). Who among these three will respond to thousands of warnings for their patients on a daily basis? The information problem does not only concern the amount of data but also the complexity of data. Physiological systems have many parameters that interact with each other, develop dynamically over time, tend to be intransparent, have delayed response, and have accumulation and homeostasis associated. There is evidence that even well-educated young adults have difficulties to understand complex systems, including their bank account, calorie budget, and the weather (Booth et al. 2000; Cronin and Gonzalez 2007; Cronin et al. 2009; Sterman and Booth Sweeney 2002). For those domains, research participants perform usually very well reading system inputs and outputs in a diagram, but they perform alarmingly poorly when judging the system’s state from inputs and outputs. For fluid management, 90% of medical students in our research could tell when a patient in intensive care had received and excreted most fluid. But only 7% could tell when the patient was most at risk of volume overload and 0% when he was most at risk of dehydration (Brunstein et al. 2010). In system dynamics research, participants are confronted


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with diagrams plotting two variables over a period of time. With increasing numbers of sensors, we would expect increasing difficulties to decide which readings are important and which are not. Given that evidence, why should users understand complex sensor readings? Issues with huge amounts of sensor data become even more urgent with rising numbers of potential users. It is comparably easy to serve a few world-class athletes or a few hundreds of patients during a clinical trial. It will be extremely challenging to serve the majority of population with diverse backgrounds, educational levels, clinical conditions, goals, etc. To summarize, current development of physiological sensors that can interact with smartphones promises to cover continuously and completely users’ physiological and behavioral measures. This will be used for monitoring users’ health conditions, for example, during exercise. At the same time, the fast-growing amount of sensor data for a user and rapidly rising numbers of potential users create a huge amount of potentially available information. Most users will not be able to understand this information, and professionals will not have the capacity for processing that much information for that many patients. 3.1.3 Guidance Desert as a Call for Intelligent Tutoring Even if users would understand the data, it is unlikely that they can apply the implications for their behavior and health. Research on health behavior has investigated for decades the motivation–action gap (Sheeran 2002) within the field of preventive medicine: many people know that they should change their lifestyle, but only a few succeed. One result from that research is that people need metacognitive support for translating their goals into specific behaviors. For example, people often start with general goals, like losing 4% body weight in 4 weeks (DietBet, version 1.2, DietBet, Inc.). For reaching that goal, they need to plan actions that are expected to result in that outcome. Not knowing which action will result in how much weight loss contributes to the motivation–action gap. The second hurdle for reaching health aims is the difficulty to backtrack success or failure to causing actions. Because physiological systems are complex, it is impossible to decide what exactly caused the weight loss when it shows on the scale after a week’s time. Sensor readings cannot solve that problem. They can protocol every cookie eaten and every step made, but they cannot bridge the utility assignment gap that guides rational skill acquisition (e.g., Fu and Anderson 2008). For example, what if calories look good during workout, but heart rate looks bad? Does it matter when to eat something? All these questions cannot be answered even with an accurate instant weight measurement. A current trend for mobile health and fitness applications is to upload sensor data to a web-based system and receive performance and progress reports on the website or via e-mails on monthly subscription basis (e.g., MapMyFitness, Runtastic). Most of these reports plot one variable at the time. They do not explain relationships and they do not conclude advice based on the data despite having the technological potential to do so. To summarize, given expected increase in user numbers and sensor data for mobile health and fitness applications as implementation of preventive medicine, there are issues to address concerning users’ motivation, needs, and preferences. For our current review of 61 mobile health and fitness applications, only 17 applications asked for user’s goals during setup (see Chapter 9). These users do not match patients or athletes. It is unlikely that increasing the amount of potentially available data will increase users’ understanding or progress toward their personal health goals. First, they have difficulties with reading, understanding, and integrating the data. Second, they will encounter problems when concluding implications of data for their behavior.

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3.2 Intelligent Solutions For solving the challenges concerning users’ characteristics, increasing numbers of users and tracked data, and understanding data for deriving implications for users’ behavior, we propose a modular approach of architecture, modeling, and tutoring as successfully practiced in cognitive modeling of human cognition and behavior. Cognitive architectures mirror our current understanding of the human mind (Anderson 2007) and provide the theoretical background for interventions by defining parameters of attention, memory, reasoning, problem solving, learning, and skill acquisition. For mobile prevention applications, health behavior theories would fill that position. Cognitive models for specific tasks complement the theory with task-specific knowledge and skills while adhering to the structural constraints of the cognitive architecture for a variety of task domains. For example, Salvucci (2006) models driver performance. Lebiere et al. (2001) model performance for an air traffic control task. Great advantage of cognitive models is that they simulate an individual user performing a specific task and produce exactly the same data as human participants do in terms of response time, mistakes, learning curve, and brain activity, for example, when learning algebra using the algebra tutor (Anderson et al. 2010). This makes it easy to judge whether we have correctly understood human performance or not. It is important to note that cognitive models simulate behavior. Software and an interface facilitate behavior. This allows modelers to focus on the aspect of interest for that task without need to model the whole body around. This means behavior change models do not need to go jogging. All they have to do is to produce the same simulated sensor data via interface as users would base on model states. Finally, cognitive tutoring systems build instruction for a domain on the cognitive architecture, on knowledge about the task domain, and on educational principles. Cognitive tutoring systems (e.g., Anderson et al. 1995) are able to provide in-time, personalized support because they have an internal model of the user integrated that mirrors a user’s current understanding and updates constantly while the user learns. For behavior change, mobile applications supported by remote servers would do the job of cognitive tutoring systems for guiding users toward their health goals. The modular approach of architecture, modeling, and tutoring permits channeling of knowledge as it accumulates in the field for behavior change in general, for specific behaviors, and for guiding instruction. It allows to split general parameters from task- or situation-specific parameters. It supports discriminating between theory and implementation via modeling or simulation. Updating one conceptual level can be done without modifying all other levels as well but impacting those downward from theory through model to tutoring system. Finally, it supports sharing the workload between mHealth community for the architecture, work or research groups for modeling, and health-care providers or companies for tutoring. It also allows the “blooming of a thousand flowers”: if app designers can easily rely on evidence and can use a common, open framework for development, applications will become more intelligent without replicating developing costs a thousand times. 3.2.1 Theoretical Framework on Behavior Change: Health Behavior Theories as Architecture Users at risk for NCDs are typical targets of preventive medicine. For this type of users, health behavior theories have investigated the factors that impact health behavior change: health behaviors are defined as aiming for good health by preventing illness when healthy


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or restoring health when ill (Kasl and Cobb 1966). Theories agree that there exist several factors impacting behavior change and/or that change happens along different states. The health action process approach (HAPA, Schwarzer 2008) integrates concepts from most health behavior theories and describes behavior change as a process along several states, from motivation through planning to action. HAPA also spells out factors impacting the transition from one state to the next: a person will progress only from an unspecific motivation to lose weight to making specific workout and diet plans, if he perceives a health risk, believes there is a solution to the problem, and feels capable to perform the associated action. A user will cope well with challenges when practicing new health behaviors if she feels capable of managing challenges and coming back to normal after relapse. Based on states and associated factors, HAPA allows to assess a user’s current state along the behavior change process and to provide support tailored for that state. These interventions have been demonstrated to be effective for several kinds of health behaviors and for participants in different states of behavior change (Craciun et al. 2012; Scholz et al. 2005; Wiedemann et al. 2012). Interventions can be potentially also implemented in mobile health and fitness applications. For adjusting support to the current state of a user concerning behavior change, an application needs to display at least a basic version of system intelligence. Schulz and colleagues suggest four dimensions for designing and evaluating Quality of Life Technology systems (QoLT; Schulz et al. 2012): system intelligence, functional domain, kind of functioning support, and mode of operating the system. System intelligence refers to the system’s ability to learn from the user and to adjust support correspondingly to the user’s needs and preferences. Schulz and colleagues argue that an intelligent QoLT system can cover the complete scale of functioning support from compensating for diminished functioning over preventing decline of functioning to enhancing normal functioning. For vision, glasses compensate for diminished functioning, while night vision systems enhance normal functioning. Current health and fitness applications tend to cover primarily one functional domain, for example, diet or exercise, and one part of the functioning continuum. Finally, a QoLT system can be operated passively, requiring no user input for operation, for example, passive tracking of performance, or it can be operated interactively, for example, requiring manual entry for consumed and burned calories. Obviously, recognizing which state a user has reached concerning health behavior change requires system intelligence, best paired with passively tracking a user’s status and interactively providing guidance. There are a few interesting implications from matching HAPA with the QoLT evaluation dimensions for this kind of user modeling with mobile technology: both frameworks describe a process of change. HAPA’s behavior change participants move from a stable state of not eating healthy, for example, uphill to adopting new healthy eating habits. Interventions help them to boost their self-efficacy and to learn required skills. The QoLT dimensions refer to constant decline of function due to aging and disability and support patients with technical means for resisting the downhill decline as long as possible. Both the HAPA theory and the QoLT evaluation criteria emphasize specific support according to a specific state of users. Both request adaptation to users’ needs and preferences. Both focus on coping with a current setting. There are also important differences fostering development of both frameworks for supporting people addressed by preventive medicine. HAPA focuses on cognitive coping and skill acquisition and ignores clinical conditions of users that might impact behavior change as well as health outcomes as rewards for behavior. Integrating those in the theory would prepare HAPA to serve aging users or users with disabilities in addition to mostly healthy

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young adults. Physiological theories might complement health behavior theories by contributing physiological parameters for behavior change. The QoLT evaluation dimensions focus on physical decline of functioning and technical support, neglecting patients’ cognitive coping strategies. Adjusting to a patient’s current physical functioning level displays global system intelligence. Taking into consideration day-to-day performance adds a layer of local system intelligence of adjusting support to current needs.


3.2.2 Modeling Health Behavior Change and Physiological Implications for Intelligent Guidance Taking together, HAPA and QoLT dimensions can guide the development of intelligent mobile health and fitness applications that support users’ efforts for changing health behaviors to reduce health risks and prevent illness and disability. It indicates modeling users on a behavioral level for anticipating needs for support. Currently, neither HAPA nor the QoLT dimensions are suitable yet to model a single user’s performance over time and provide appropriate, in-time support. The QoLT dimensions have not been used for modeling patients’ performance. HAPA models typically use path analysis (e.g., Chiu et al. 2011) for predicting who will change on level of a patient cohort. Similarly, intervention efficiency has been studied on level of patient cohorts (e.g., Wiedemann et al. 2012). The path analytic method has been vital for spelling out direct and indirect links between impacting factors (e.g., Edwards and Lambert 2007), like participants’ action self-efficacy, and resulting behavior change during preparation stage. This method is also suitable for discriminating between relevant and irrelevant sensor data sets on the level of a patient or user cohort. However, several authors (Boorsboom et al. 2003; Dunton and Atienza 2009; Ogden 2003) have argued that health behavior theories with their current modeling approach are not suitable for providing intelligent support for behavior change on an individual user level. Riley et al. (2011) propose using dynamic system models as modeling frameworks for health behavior theories. They also provide evidence for effectiveness for preventing children from developing conduct disorder with family counseling using this approach (Navarro-Barrientos et al. 2010). In addition, HAPA does not cover health implications and barriers based on clinical conditions or injury. Gortmaker et al. (2011) propose using quantitative modeling methods for understanding factors contributing to the obesity epidemic from individual level up to population level, including energy gap models. This modeling approach can be used for understanding the implications of behavior on body weight given complex systems with intransparency, accumulation, and delayed response. Modeling users’ behavior for intelligent behavior change support is only half of the story. Cognitive modeling can focus on cognition and behavior only, because concerned behavior typically results in acquired skills and learned knowledge. Health behavior change results in learning only as a side effect. The main outcome of health behavior is impacting a person’s health. Also, users are not primarily interested to get learning support. They need guidance for reaching their health goals. Therefore, modeling health behavior change needs to integrate social, cognitive, behavioral, and physiological aspects of this process. 3.2.3 Adaptive Visualization and Simulation for Providing Intelligent Guidance for Health Behavior Change System dynamics research shows that people have difficulties understanding complex systems, including calorie balance or health in general (e.g., Cronin et al. 2009). Diagrammatic


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reasoning research shows that people have difficulties understanding even simple graphs (e.g., Mazur and Hickam 1993). This implies users of mobile prevention applications will not be able to read, understand, and integrate physiological sensor data and the physiological system behind those readings. Simulation has been demonstrated to be an efficient method for teaching complex systems to professionals in several domains, including aviation (e.g., Hays et al. 1992), operating power plants, finance, policymaking, and surgery (e.g., Milburn et al. 2012). Simulation allows exploring complex systems in a safe environment, without time constraints, if needed, transparent and without consequences. There is also evidence that simulation helps novices to understand complex systems (e.g., Gonzalez and Dutt 2011; Vollmeyer et al. 1996). Therefore, modeling the physiological system is important to link health behaviors to health outcomes and for allowing users to explore consequences of their behavior via simulation. Seeing an avatar aging faster or slower depending on health behavior choices might be a strong motivator for implementing a healthier lifestyle even if health outcomes show only much later, like for weight loss in obese patients. There exist several promising proposals for simulating users (e.g., see Jiang et al. 2012 or Macal and North 2007 for an overview on agent-based modeling). As research participants can learn to understand complex systems, participants in diagrammatic reasoning research fail systematically for some aspects of diagrams and systematically excel for others indicating that there is some hope for users understanding their physiological sensor readings. First, knowing the task domain can partially compensate for missing diagrammatic literacy (e.g., Mazur and Hickam 1993). Second, starting with Larkin and Simon (1987), researchers have discriminated between perceptual and conceptual processing of diagrams (e.g., Kim et al. 2000). Perceptual processing covers information that can be directly read out of a diagram. For our research (Brunstein et al. 2010), that was calories consumed and expended during a time period. Indeed, participants performed very well for that information directly displayed in a diagram. Conceptual processing covers information that cannot be directly read out of a diagram but can be inferred from what is given. If there are no other impacting variables, weight directly results from current weight plus calories consumed minus calories expended. This kind of inference did not work well for novices in our study. The US Department of Agriculture (USDA) has retired the famous food pyramid in their campaigns in 2011 and has replaced it with a plate displaying the proportions of nutrition groups for a healthy diet (USDA Center for Nutrition Policy and Promotion 2013) consumers associate a plate more with eating than a pyramid predicting more transfer to America’s breakfast tables than the pyramid has had. The difference between perceptual and conceptual processing compares to foreground and background in a picture. People perform very well for information in the foreground and less so for information in the background. No artist would portrait a king behind his castle. The other way around, only who knows the location well can describe the castle behind the king in detail. If users are interested in health outcomes, why would we display sensor readers in the foreground and let them infer the health implications themselves? Therefore, progress or monitoring displays should make relevant information salient, explicit, and in the foreground and hide not relevant information in the background. What is relevant depends again on the users’ needs. If they want to track their progress in weight loss, current weight plots might be sufficient. In contrast, for planning behavior and backtracking perceived health outcomes to actions, the relationship between behavior and expected or experienced outcomes is important and needs to be displayed. “How much difference in body weight can I expect when jogging for 30 min instead 20 min?” “The scale looks still as bad as in the beginning of the program. When can I expect to harvest results?” HAPA

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understands action planning as mental simulation of the concerned behavior (Schwarzer 2008). Visualizations that support this kind of mental simulation should display related parameters. Most likely relevant parameter will integrate several sensor readings over time in higher-order concepts and not present individual readings given the complexity of the physiological, behavioral, and social systems. Technical solutions for integrating sensor data in a meaningful way start to emerge (e.g., Rodriguez-Molina et al. 2013). Larrik and Soll (2008) describe a successful implementation of making relevant information salient in diagrams. When presenting fuel efficiency of cars in miles per gallons, most college students did not understand that greatest improvement comes from retiring least efficient cars. This misconception disappeared when presenting fuel efficiency in gallons per mile. Displaying relevant information that enables users to understand the complex system of behavior, health, and context is the first part of intelligent QoLT systems in general and mobile health and fitness applications in particular. As important is guiding the user toward the most efficient way for reaching his or her health goals. Intelligent health tutoring system can generate this guidance based on the theoretical framework or architectures and from models on relevant health behaviors. Simulations with the current user parameters can identify one or a few optimal paths through the problem space of changing health behavior and can provide users with corresponding suggestions and motivation support.

3.3 Conclusions Building an architecture reflecting our current understanding of users’ health behavior change and physiological processes needs to be a mHealth community-wide project. Platforms like Open mHealth (Chen et al. 2012) might facilitate that project. Modeling specific aspects of health behaviors and/or physiological processes can be a task for research groups who implement evidence from empirical studies and generate specific and detailed hypotheses for upcoming studies. Finally, app design concerns both, all-around talent as virtual personal trainers on a healthy lifestyle and special purpose applications, for example for counting steps or spelling out nutrition values. These have to be delivered by healthcare providers or commercial suppliers of the mobile app stores. Patrick et al. (2008) and Collins (2012) advertised the potential of mobile technology for intelligent support of patients and citizens. By guiding technological process with theory and evidence-driven concepts, we hopefully do not need 5 more years to get there. CASE SCENARIO 1 Rodriguez et al. (2013) describe a wireless software network that integrates sensor data from users during workout for producing warnings, for example, if body temperature rises above the threshold given the current environment temperature. QUESTION: Why is this application better than simply displaying body temperature readings next to graphs on heart rate, oxygen level, and burned calories? The answer is available at the end of the book in “Answers to the End-of-Chapter Questions.”

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CASE SCENARIO 2


Mr. Smith, 50 years old, describes himself as a couch potato who plans to become a little more active to keep up with his grandchildren. He recently bought a mobile phone application WALK! that tracks every step he makes and syncs in several physiological sensors, including heart rate, and performance measures, including distance, pace, and time. After his walk through the park, WALK! presents an impressive analysis of his performance with map, color-coded fast- and slow-track aspects of his workout. Based on his readings, the program recommends to spend 5 min cooling down and shows a male in his 40s performing the stretching exercises as a model. QUESTION: Concerning guiding Mr. Smith’s through his fitness program, which aspects should be covered by the theory, by modeling, and by the tutoring system? The answer is available at the end of the book in “Answers to the End-of-Chapter Questions.”

3.4 Future Directions Users, who are neither athletes nor patients, increasing amounts of available sensor data and currently missing guidance for health behavior change, pose challenges for future mHealth applications. For mastering these challenges, we propose a modular framework with theory, modeling, and intelligent tutoring as practiced in cognitive science. This approach clearly discriminates between theoretical knowledge acquired in the field and specific mobile applications designed to foster behavior change. That split allows reducing effort and costs and discrimination between general theoretical knowledge and taskspecific evidence. There is a need for modeling for bridging the theory–application gap. Without modeling, theories tend to be too unspecific to be implemented in applications. With models implementing the theory for specific tasks, there is a structured translation tool between theory and applications that allows transfer of knowledge in both directions.

Questions 1. How do users of mobile health and fitness applications differ from patients with chronic conditions or athletes? 2. Why do some researchers warn about a potential data tsunami in respect to mHealth sensor readings? 3. Why is it not good enough for mHealth applications to display information, like sensor readings or nutrition values for food, but also provide guidance on action? 4. Why do we need a theory on behavior change and physiological processes when designing mHealth applications?

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5. How does a user model for a specific health behavior complement health behavior theories? 6. Why can simulation of health behavior and physiological processes help users to take action?


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