Implementing Behavior Change:: Evaluation Criteria ...

9 Implementing Behavior Change: Evaluation Criteria and Recommendations for mHealth Applications Based on the Health Action Process Approach and the Quality of Life Technology Framework in a Systematic Review Angela Brunstein, Joerg Brunstein, and Michael K. Martin CONTENTS 9.1 Introduction......................................................................................................................... 134 9.1.1 Health Action Process Approach......................................................................... 136 9.1.1.1 Assessing Phases of Change and Associated User Groups............... 136 9.1.1.2 Identifying Relevant Factors Impacting the Process of Behavior Change....................................................................................................... 137 9.1.2 Quality of Life Technology Evaluation Dimensions......................................... 137 9.2 Methods................................................................................................................................ 138 9.2.1 Selecting mHealth Applications........................................................................... 138 9.2.2 Classifications for HAPA Components and QoLT Dimensions....................... 140 9.2.2.1 Assessing Health Status.......................................................................... 140 9.2.2.2 Assessing Status of Behavior Change................................................... 140 9.2.2.3 Intention State Support........................................................................... 140 9.2.2.4 Risk Communication............................................................................... 140 9.2.2.5 Resource Communication....................................................................... 144 9.2.2.6 Planning State Support........................................................................... 144 9.2.2.7 Action Planning Support........................................................................ 144 9.2.2.8 Coping Planning Support....................................................................... 144 9.2.2.9 Action State Support................................................................................ 144 9.2.2.10 Action Initiative Support........................................................................ 144 9.2.2.11 Action Maintenance Support................................................................. 145 9.2.2.12 Relapse Prevention................................................................................... 145 9.2.2.13 Targeted Functional Domain................................................................. 145 9.2.2.14 Kind of Functioning Support................................................................. 145 9.2.2.15 Mode of Operating the System.............................................................. 145 9.2.2.16 System Intelligence.................................................................................. 146 9.3 Results.................................................................................................................................. 146 9.3.1 Exercise Performance Tracking............................................................................ 146 9.3.2 Running Programs................................................................................................. 147 9.3.3 General Workout Tools........................................................................................... 147 9.3.4 Diet Tools................................................................................................................. 149 133

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mHealth Multidisciplinary Verticals

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9.4 Discussion............................................................................................................................ 151 9.5 Limitations........................................................................................................................... 152 9.6 Case Study........................................................................................................................... 153 9.7 Future Directions................................................................................................................ 153 9.8 Conclusions.......................................................................................................................... 153 Case Scenario................................................................................................................................ 154 Questions....................................................................................................................................... 154 References...................................................................................................................................... 154

9.1 Introduction In this review of current mobile health and fitness applications, we focus on mHealth applications’ potential for preventive medicine interventions. According to the American College of Preventive Medicine (ACPM), this discipline aims to “… protect, promote, and maintain health and well-being and to prevent disease, disability and death … [in] individuals, communities, and defined populations” (American College of Preventive Medicine 1998). Currently, the biggest application area for preventive medicine is noncommunicable diseases (NCD). In 2008, NCD accounted for 63% of global death (WHO 2010) and prevalence of NCD is predicted to increase significantly over the next years. According to the global status report on NCD (WHO 2010), the top four killers are cardiovascular disease, cancers, diabetes, and chronic lung diseases. Risk factors for these conditions are strongly related to lifestyle and health behavior, including unhealthy diet, physical inactivity, and tobacco use (WHO 2009). Increasing protective health behaviors in patients is related to reducing health risks, improving health, and prolonging expected life span (e.g., Ahmed et al. 2013). Therefore, the World Health Organization (WHO 2010) has published recommendations for different health behaviors, including healthy diet and exercise, for reducing risk factors for NCD. For children, the WHO (2010) recommends at least 60 min of at least moderate physical activity a day and for adults 150 min of moderate or 75 min of vigorous physical activity during the week with aerobic activity sessions lasting at least 10 min. Despite consensus and evidence, majority of adults do not adhere to recommended levels of health behaviors, for example, of physical activity (Guthold et al. 2008; WHO 2002). In 2009, the WHO published a review of interventions that have been demonstrated to increase physical activity and concluded based on 937 diet studies and 776 physical activity studies that best interventions addressed both physical activity and healthy diet, and not just either of the two. In addition, involving users is more promising than just prescribing healthy levels. On a conceptual level, health behavior theories have been shown, first, to predict who is likely to succeed in improving their lifestyle and who will not (e.g., Armitage and Conner 2001). Therefore, these theories can be used for assessing potential users of mHealth applications. Second, they have successfully identified relevant dimensions contributing to behavior change. These dimensions need to be addressed in interventions when fostering behavior change. Mobile health and fitness applications could tap into this potential of health theories for increasing health behavior and reducing risk of NCD. When implementing interventions using mobile technology, mobile phone applications can use intrinsic properties of mobile technology to support behavior change, namely, that mobile phones are easily accessible, online, and interactive. These properties allow mobile technology to bridge spatial and temporal distance. Instead seeing family physician every


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other month, mobile phone applications can provide guidance for every meal and every workout. This means that advice becomes more specific: Instead of “Reduce fat intake!” it can say “Eat this, not that!” At the same time, reported health behavior becomes more specific and more accurate. Instead of a patient reporting back: “… the last month went mostly ok,” a mobile application can report every step and every consumed calorie with a timestamp. This kind of data permits an accurate analysis of potential challenges and correspondingly designing better interventions. Evidence for effective mHealth interventions starts to accumulate (e.g., de Jongh et al. 2012; Tomlinson et al. 2013). At the same time, there is increasing awareness of the need for safe and valid applications given the vast amount of applications available on the commercial market resulting, for example, in the U.S. Food and Drug Administration’s guidance for mobile medical applications (2013). As mentioned earlier, health behavior theories can assist creating efficient mobile applications by providing means for assessment, relevant dimensions to be covered, and evidence-based interventions. There are several health behavior theories in the field, each associated with evidence and all of them with partially overlapping concepts. The most prominent theories are the health action process approach (HAPA, Schwarzer 2008), the health belief model (HBM, Becker and Rosenstock 1984), the theory of planned behavior (TPB, Ajzen and Fishbein 1980), and the transtheoretical model (TTM, Prochasta and Velicer 1997). They all describe the intention–behavior gap (Sheeran 2002): not all people who intend to improve their health behavior actually succeed. Theories also agree that different factors determine who will succeed and who will not, and most of them have been used to create successful interventions for improving diverse health behaviors in patients. For this evaluation of current mHealth applications, we chose the HAPA as a hybrid model that unifies two lines of health behavior models, models covering factors impacting health behaviors and models focusing on the process of change. HAPA has been designed to cover most relevant factors from earlier theories. As a hybrid model, it stresses the process of changing behavior as well as the factors contributing to different states during that process. HAPA has been used for predicting and impacting diverse health behaviors, including dental flossing (Schuez et al. 2006, 2009), breast self-examination (Luszcynska and Schwarzer 2003), physical activity after cardiac rehabilitation (Scholz et al. 2005), and weight loss (Renner and Schwarzer 2005). For these application areas, HAPA’s framework and interventions have been extensively tested using statistical modeling methods on empirical data. When applying HAPA components for evaluation of mobile health and fitness applications, these theoretical concepts need to be translated into functionality of applications. In 2012, we performed this exercise of translating components of HBM, TPB, and TTM into evaluation criteria and used those for a systematic review of 14 out of 100 iTunes’ Health & Fitness applications (Brunstein et al. 2012). Similarly, Azar and colleagues (2013) used criteria based on HBM, TTM, TPB, and social cognitive theory (Bandura 2001) for evaluating 23 free, stand-alone diet applications out of 200 iTunes Health & Fitness applications. Here, we have chosen HAPA for our evaluation because it unifies theoretical constructs from different health behavior models in one approach. Compared with the eclectic pooling of single components from different theories, a unified framework is more powerful because it allows investigating interactions between factors over time. Earlier reviews have identified very little use of health behavior theories in current applications. Using a unified framework like HAPA goes beyond detecting missing coverage by permitting recommendations on how to implement relevant factors in mobile applications for different user groups and for different time points along the process of adopting healthier habits. Even if a mobile application would include all relevant HAPA criteria, it would be useless without minimal standards for usability. Probably some people would stop eating cookies if they had to monitor their diet, and entering a cookie would take 20 clicks and searching

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several databases. There exist several approaches for designing successful mobile applications (e.g., DeLone and McLean 2003). We decided to use Schulz and colleagues’ interdisciplinary approach of designing and evaluating Quality of Life Technologies (QoLTs; Schulz et al. 2012) because this approach is patient centered and can be used for design and evaluation of patient support systems along four dimensions. In the following, we describe the HAPA model and the QoLT dimensions as used in our review more in detail.


9.1.1 Health Action Process Approach HAPA discriminates between different groups of users based on phases during the process of behavior change. This is important because users’ needs for support change during that process. In addition, HAPA defines several factors impacting the process. Each factor is relevant for user’s behavior change during a specific phase or state of the process, but not during other phases. For mobile health and fitness applications, the HAPA phases can be used for assessing users, and the factors can be used for providing appropriate support for users given their current state. 9.1.1.1 Assessing Phases of Change and Associated User Groups HAPA discriminates two broad phases of change, a motivation or goal setting phase and a volition or goal pursuit phase. Users during the motivation phase might know that they need to lose weight and therefore have to become more physically active. As long as they have not started planning behavior change, HAPA calls these users nonintenders. This label is a little misleading because the motivation phase includes a state of intention. Basically, nonintenders move from a preintentional state to an intention state within the motivation phase resulting in a general but unspecific aim to change their health behavior. Once users have decided to actually change their lifestyle correspondingly, they enter the volition phase that includes a planning state and several action states. Users during the planning state are called intenders. During this state, intenders define the when, where, and how of behavior for action planning. They might subscribe to a fitness club or buy running shoes. In contrast to intention state, intenders plan to start workout next Monday, not sometime soon. In addition to spelling out action plans for workout and diet, intenders should think about potential barriers when changing their lifestyle during coping planning. Maybe it is easier to keep a healthy diet when eating alone, but not during family meals or eating out with friends. They should also identify strategies for coping with these challenges in advance. One option to combat grandma’s pies is eating a light meal before joining the feast for feeling less hungry. Once users have started to enact their plans, they enter the action state and are called actors. Within the action phase, HAPA discriminates between early implementation of new behaviors, the initiative state, later building of habits during maintenance state, and maybe recovery state after relapse. The initiative state covers the first weeks in the gym. During this state, actors acquire necessary skills. Once actors have become accustomed to new behaviors and start to build and maintain a habit of exercising over a period of months or years, they are in a maintenance state. From there, they might remain in maintenance state; they might leave once they have reached their personal goal, for example, fitting into the wedding dress; or they might suffer a relapse. After relapse, they might pass through a recovery state and kick off another planning, initiative, or maintenance state. According to HAPA, states are not fixed and users can move forward and backward between states several times. It is important for mobile health and fitness applications to assess a user’s current state within the process of behavior change for providing appropriate support for that state.


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9.1.1.2 Identifying Relevant Factors Impacting the Process of Behavior Change Users in different states have different needs for support: nonintenders might profit from motivational support for setting personal weight loss goals. That support would be wasted on intenders who have already set their goals. Intenders and actors during initiative state might profit from detailed instruction on how to perform exercise without injury. This would be useless for users in maintenance state who are already exercising for months. HAPA has identified five factors impacting transitions between states. Nonintenders will come up with specific weight loss goals only if they believe that they can perform exercise (i.e., action selfefficacy), if they perceive a health risk if not exercising (i.e., risk perception), and if they believe that exercising could reduce that health risk (i.e., positive outcome expectancies). According to HAPA, risk perception and outcome expectancies impact only intention, but not planning or action states. In contrast, action self-efficacy also impacts planning. During action state, action self-efficacy loses impact as predicting variable, probably because everybody who is exercising knows now by experience that they can do. From planning state on, coping selfefficacy, as the belief to be able to overcome barriers, becomes important. Users who score high on coping self-efficacy are more likely to engage in coping planning. They might decide going jogging on Monday, which is their original rest day, if they have an important business meeting on Tuesday and go back to normal on Wednesday. Not surprisingly, these users will also cope better with barriers when they materialize. Therefore, coping self-efficacy also impacts the action phase. Finally, recovery self-efficacy concerns users’ belief whether they can come back to action after relapse. People with high recovery self-efficacy tend to attribute relapse to a difficult situation or setting. They perceive relapse less dramatically than people who score low on recovery self-efficacy and attribute relapse to their own failure. They are also more likely to master relapse and return quickly to normal. Integrating phases and impact factors, HAPA has been successfully used to design interventions. Nonintenders have profited from risk and resource communication (e.g., Cranium et al. 2012). A mobile application might calculate the body mass index (BMI) for a user and list the associated health risks. It might also suggest small changes in behavior and list associated health benefits. Intenders have benefited from an exercise for action and coping planning that provides skills for translating intentions into actions (e.g., Wiedemann et al. 2012): they were asked to write down a few specific goals with spelling out the when, where, and how for action planning. They were also asked to envision a few specific challenges and to identify solutions for those challenges for coping planning. A mobile app might ask for specific goals during setup and adjust suggested workout to those goals. In addition, the app might suggest automatic reminders during setup, for example, for drinking water. Actors usually need very little support, but might profit from relapse prevention and management when translating newly acquired behaviors into habits. A mobile application might present frequently encountered barriers and strategies for overcoming those barriers. These will be particularly important in the context of quality of life dimensions. 9.1.2 Quality of Life Technology Evaluation Dimensions Schulz and colleagues’ framework (2012) has been used for designing QoLT systems and for evaluating existing QoLT systems. The framework suggests four dimensions for defining outcome measures and for evaluating ease of operation in general. The first dimension is the functional domain that an application is designed for. That domain could be as general as weight loss or as specific as yoga instruction. Specifying the functional domain is necessary for defining outcome measures. It also prevents comparing


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apples with oranges. Therefore, we categorized health and fitness applications by functional domain before comparing their functionality. Second, Schulz and colleagues differentiate kinds of functioning support along a continuum between compensating diminished functioning, like a wheelchair for restoring mobility, and enhancing normal functioning, like a bicycle for going beyond a person’s normative physical mobility. In between these two endpoints, systems can help to maintain normative functioning or prevent decline, like regular workout or physiotherapy. Most mobile applications support maintaining functioning and preventing decline. Some athletic applications support enhancement of normative functioning. Applications for management of clinical conditions help to compensate for diminished functioning. Schulz and colleagues state that highly intelligent systems address needs of users throughout the continuum. Closest to that ability to adapt to users’ functioning level are series of applications, like Couch to 5K, 5K to 10K, 10K to Half Marathon, and Half Marathon to Marathon running applications that span a range from prevention to enhanced functioning, but do not include compensation. Third, Schulz and colleagues address the mode of operating the system. QoLT systems can be operated passively without requiring user input or interactively requesting user input for operation. Passive calorie tracking is obviously more convenient than manually entering every cookie. On the other hand, it can be very frustrating to discover that only 200 steps have been tracked after hiking for hours in the woods because of poor GPS coverage. Therefore, a hybrid model with options for passive or (inter-)active operating technology might be optimal for mobile health and fitness applications. Fourth and finally, Schulz and colleagues address the system intelligence as the ability to learn from a user and to adapt to a user’s needs and preferences. For example, some workout planning tools (e.g., Gymprovise, Gymprovise) adjust a suggested workout to available equipment and suggest alternatives if currently not available. We found it useful to discriminate between global and local system intelligence. Many mobile calorie tracking applications globally adjust their calculations to a person’s body weight, for example, when displaying burned calories for a workout. Applications with local system intelligence can adapt to current performance during workout or diet. This is essential for relapse prevention and management. Targeted functional domain, kind of functioning support, passive or interactive mode of operating the system, and system intelligence do not assess the complete spectrum of mobile applications’ usability, but they provide a useful proxy for assessing what is currently on the market and what is not yet.

9.2 Methods 9.2.1 Selecting mHealth Applications We have reviewed application descriptions for mobile applications at the iTunes Store and the Google Play App Store that are listed under the Health & Fitness categories as of June 21, 2013. I wish we could update the analysis. But this would mean to redo the complete research. It might be that some of the apps that we analyzed cannot be found anymore and that others that we did not consider are relevant now or new on the market. Nevertheless, the framework would apply to the recent apps as well and the examples should sufficiently illustrate how to use the framework for evaluating recent applications. Within those categories, we selected the list of top free and paid applications. This resulted in 571 health and fitness



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applications for Mac and 400 medical and 400 health and fitness applications for iPhones. For Androids, we gathered 480 medical and 480 health and fitness applications. For both platforms, we chose the U.S. stores resulting in total 2331 applications in the first selection list. We applied several rounds of filtering before generating the final list based on titles, publishers, application description, and screenshots on their store website. Mostly, we excluded duplicates and applications that were either unrelated, did not utilize mobile technology, were not directly related to relevant health behavior of humans, or did not work on our devices. During the first filtering, we included applications that are related to health behavior, have an English title and description, and are designed for humans. We excluded nonEnglish titles or descriptions, medical education materials for exam preparation, medical reference and translation tools, applications for animals, games without health content, beauty tips, and applications that seem to be misclassified, like egg timer. After the first filtering, there were 1385 applications left. During a second filtering, we eliminated applications that had several versions across and within platforms. For example, we kept only one copy of applications that have a version for iPhone and Android. We also kept only one version for applications that have several siblings. For example, Daily Workout (Daily Workout Apps) exists for arm, butt, leg, cardio, and yoga. Similarly, several running apps have subversions for couch to 5K, 10K, Half Marathon, and Marathon. If possible, we preferred general applications (e.g., Daily Workout, Daily Workout Apps) before specific applications (e.g., Daily Leg Workout, Daily Workout Apps) and applications that had different programs integrated within the application (e.g., from couch to 5K up to Half Marathon to Marathon) before applications for a single program. We categorized applications based on the QoLT evaluation dimensions according to their targeted functional domain. Thereafter, we excluded functional domains that are not central to health behavior, for example, hypnosis and meditation, or that target specific situations in a user’s life, like pregnancy or baby applications. After filtering, we had 435 applications left with a majority from exercise (N = 187) and diet (N = 132) but also health management, including monitoring physiological measures (N = 47), for example, blood pressure; adherence (N = 18); diabetes management (N = 14); sleep, relaxation, and stress management support (N = 14); substance cessation (N = 12), mainly alcohol and smoking; patient education (N = 6); and rehabilitation support (N = 5). We decided to focus on diet and exercise as the most central health behaviors impacting people’s weight loss and health, as covered best in the literature and based on the biggest numbers of applications covered in the Health & Fitness applications stores. Within the exercise category, we discriminated between general workout (N = 85), including short-duration workout, HIIT, and workouts of the day (WOD); distance sports programs (N = 50), like running and biking; activity tracking (N = 13); bodybuilding (N = 11); yoga and Tai Chi (N = 13); warm-up and stretching (N = 4); and lifestyle (N = 5), including weight loss and fitness assessment. We excluded six applications that were either unrelated after detailed inspection, like music selection when jogging, or had too few applications for creating a subcategory. Within the diet category, we discriminated between specific diet applications (N = 26), like Paleo diet, food databases (N = 43), calorie trackers (N = 60), and comprehensive weight loss programs (N = 3). For both exercise and diet, we found an impressive range of functionality and creative use of mobile technology potential among applications. For example, for measuring heart rate at rest and during exercise, there exist comparably accurate sensor measures next to iPhone camera estimates. We also found significant overlap in functions across applications. For example, there exist several GPS trackers with only marginal differences that would look all the same in our analysis. Therefore, we applied an approach of fishing for features for representing the range of functions and features that are currently on the market for presentation in

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this chapter. For each subcategory, we identified one or two candidates with basic functionality, for example, a manual entry running log. In addition, we sampled advanced options, like pedometers, GPS trackers, integration of sensor data, optional life tracking and sharing, colorcoded performance analysis, and race against earlier runs or opponents. Tables 9.1 through 9.3 include evaluation results for 41 exercise applications and 21 diet applications. We downloaded these applications from the iTunes or Google Play shop and installed them onto an iPhone 4s, iPod touch 32 GB, or onto a Nexus 4 (16 GB) phone or an Acer Iconia B1 tablet. After inspecting the applications and using it for its designed purpose for a few days, we classified them concerning HAPA coverage and QoLT dimensions.


9.2.2 Classifications for HAPA Components and QoLT Dimensions Based on Schwarzer (2008) for the HAPA components and on Schulz and colleagues (2012) for the QoLT dimensions, we developed the following operational definitions for evaluating current health and fitness applications. We revised these definitions within the workgroup and finally reached an interrater reliability of 97% for a subgroup of 10 applications rated independently. 9.2.2.1 Assessing Health Status Assessing a person’s health status is relevant for communicating risks to nonintenders (HAPA) and is a prerequisite for global system intelligence (QoLT dimensions). We classified an application as assessing initial health measures if asking at least for a user’s age, gender, weight and height during setup, and better also for clinical conditions. 9.2.2.2 Assessing Status of Behavior Change HAPA’s strongest claim is tailoring behavior change support according to a user’s needs based on their state along the process of behavior change. We classified an application as assessing behavior change states (BCSs) if at least asking for users’ goals. During this stage, we did not investigate whether applications use that information for tailoring support. This feature was evaluated under system intelligence. 9.2.2.3 Intention State Support HAPA recommends for intention state risk and resource communication. Most health and fitness applications do not serve nonintenders by definition, because users start to search for appropriate applications during planning state after setting a goal. 9.2.2.4 Risk Communication When discussing pros and cons of a critical health behavior, risks are associated with not performing that health behavior. Communicating injury risk associated with performing exercise does not qualify for risk communication. We classified applications as communicating risks if they addressed health risks associated with poor diet or physical inactivity.

Evaluated Performance Tracking and Walking Program Applications, with Supported Platforms


Intervention Applications Application (a) Accupedo-Pro B.iCycle Endomondo Heart Rate Pro MapMyFitness+ MyFitnessCompanion Nexercise Road Bike Pro SmartRunner Pro Walkmeter WOD Tracker Pro (b) 5K Runner B210K Pro FIRST Get Running Higdon Novice1 26.2 Run a 10K PRO! Zombies, Run!

I

II

Functioning Support

III

Mode of Operating

Platform

Assessment

Publisher

iT

An

H

BCS

Ri

Re

AP

CP

IS

MS

RP

Pr

En

I

T

Intelligence

Corusen LLC Valley Development Endomondo Runtastic MapMyFitness Inc. myFitnessCompanion Nexercise Runtastic APPSfactory Abvio Inc. Pete Wood Clear Sky Apps LTD Guy Hoffman Bryan Catron Splendid Things Bluefin Software, LLC Red Rock Apps Six to Start

1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1

1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1

1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0

0 0 0 0.5 1 0 1 1 0 1 0.5 1 0.5 1 1 1 0.5 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0

0 0 0 0 0 0 A 0 0 P 0 P P P P P P P

P P P A P+A P+A P P P P A 0 0 0 0 P P P

L 0 0 0 0 0 L G+L 0 G 0 0 0 G 0 0 0 0


TABLE 9.1

Notes: iT, iTunes; An, Android; initial health (H) and BSC evaluation; offered interventions (I or the intention state: Ri, Re, risk and resource communication; II or the planning state: AP, CP, action and coping planning support; III or action states: IS, initiative support; MS, maintenance support; RP, relapse prevention); kind of functioning support (Pr, prevention; En, enhancing normal functioning); active versus passive mode of operating the system (I, instruction; T, tracking); system intelligence (G, global; L, local).

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142

TABLE 9.2 Evaluated Workout Applications, with Supported Platforms Intervention

Application (a) 7 min Workout Daily Workouts Gorilla (b) BYFWM!! Just 6 Weeks Pushups PushUps PRO (c) Daily Yoga Stretch Guru: Run Taichi8+16 (d) Fitness Caynax HIIT PRO Nike Training (e) BBB Gym Genius Gymme Gymprovise myWOD WOD Programmer Workout Hero (f) U.S. Navy PFA Calculator SuperBetter

Platform

Assessment

I

II

Functioning Support

III

Mode of Operating

Intelligence

Publisher

iT

An

H

BCS

Ri

Re

AP

CP

IS

MS

RP

Pr

En

I

T

G

UOVO Daily Workout Apps Heckr LLC Creative Freaks Just Do Inc. Clear Sky Apps LTD Runtastic DailyYoga.com Lori Bryan Living ChuFengChin Plus Sports Caynax Nike WorkoutRoutines AppBrains Matteo Pizzorni Gymprovise theDreamWorkshop CrossfitHardcore Storeboughtmilk LLC Crash Test Dummy SuperBetter Labs

1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 1

0 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 0

0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 1 0

0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 1

0.5 0.5 0.5 0.5 0.5 1 0.5 1 1 0 1 1 0.5 1 1 1 1 1 0 1 0 1

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1

1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 0 1

0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0

0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0

P P P P P P P P P P P 0 P P 0 A A P 0 P 0 A

0 0 0 A A A P 0 0 0 P+A 0 0 A A A P P A A A A

0 0 G L G 0 0 0 0 0 0 0 0 0 0 G L 0 0 0 0 G+L




Applications

Evaluated Diet Applications, with Supported Platforms Intervention


Applications Application (a) 3 Day Diet 17 Day Diet Complete Fast Metabolism Diet Low Fodmap Diet HCG FiiT Chick (b) Eat This! Green Smoothies NxtNutrio Paleo Central Points Calculator (c) CaloryGuard Pro Carb Master CarbsControl Cronometer DietBet Nutrition Menu Nutritionist (d) Bodybuilding Diet My Diet Coach - Pro Noom

Platform

Assessment

I

II

Functioning Support

III

Mode of Operating

Publisher

iT

An

H

BCS

Ri

Re

AP

CP

IS

MS

RP

Pr

En

I

T

Intelligence

Realized Mobile LLC Realized Mobile LLC Random Digital Inc. CEMA Appilicious Brett Campbell Rodale Inc. Raw Family Nxtranet Inc. Nerd Fitness Ellisapps Inc. BlueBamboo Deltaworks Coheso Inc. BigCrunch Consulting DietBet Shroomies Nutritionist Bodybuilding Diet - Pro InspiredApps Noom Inc.

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1

1 1 0 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1

0 1 1 1 1 0 1 0 1 0 0 1 1 0 1 1 0 1 1 1 1

0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1

0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0

0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1

1 1 1 1 1 1 1 1 0 0.5 0.5 0.5 0.5 1 0.5 1 0.5 1 1 1 1

0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1

0 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 1

0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P P P P P P P P P P 0 P 0 P P 0 0 P P P A

P A A A+P A 0 0 0 0 0 A A A A A A A A+P A+P A A+P

0 0 0 L L 0 G+L 0 G+L 0 0 L 0 0 L 0 0 G G 0 G+L

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TABLE 9.3

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9.2.2.5 Resource Communication Benefits associated with physical activity and healthy diet complement health risks associated with inactivity and poor diet. According to HAPA, positive outcome expectancies include health benefits, but also expected positive social or emotional outcomes. We classified applications as addressing resource communication if they addressed benefits of performing exercise or eating healthy. 9.2.2.6 Planning State Support


HAPA recommends for planning state to provide metacognitive support for action planning and coping planning by translating goals into action. 9.2.2.7 Action Planning Support Action planning translates goals into behavior by specifying the when, where, and how of the concerned behavior. Action planning support can either provide structure for this process or suggest options for this mapping. We classified applications as supporting action planning if they can be used to envision exercise or diet as specific as possible before actually doing it. As a special case, we coded applications that can be misused for planning by fast-forward simulations as action planning support even if the application was not designed to support planning. 9.2.2.8 Coping Planning Support While action planning spells out parameter for critical behavior, coping planning concerns expected barriers. Coping planning support can either provide structure for this process or suggest typical barriers and solutions. We classified an application as supporting coping planning if mentioning or asking for potential challenges before performing the concerned health behavior. 9.2.2.9 Action State Support According to HAPA, users in action phase do not need much action support, but might profit from relapse prevention and management support. Because actors solve different tasks during initiative, maintenance, and recovery state, they are expected to change in respect to their needs and preferences during these states. 9.2.2.10 Action Initiative Support During the action initiative state, users start to perform the desired behavior for a few days or weeks. For the user who planned to join the health club, this is the first few weeks in the gym. During this state, users acquire required new skills. Correspondingly, users will profit from detailed and specific instruction, demonstration, and feedback. We classified an application as suitable for initiative action state if it provided detailed instruction or demonstration on what to do, for how long to do, and how to do. In contrast to action planning, this instruction should be in time and allow to perform an exercise along with the model.


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9.2.2.11 Action Maintenance Support During the maintenance state, users continue to perform the desired behavior over a longer period of time in terms of months to years. During this state, users form and maintain new habits. Users do not need detailed instruction anymore because they have sufficiently learned the procedures already. We classified an application as suitable for maintenance state if it provided support for performance over longer periods of time, including tracking current performance and logging performance over time, analyzing past workouts, and providing detailed feedback for increasing performance. Applications that fight boredom in the gym might also provide maintenance support.


9.2.2.12 Relapse Prevention Several factors impact how well a user will master challenges. As for coping planning, users can profit from metacognitive support for anticipating and mastering barriers. As for the intention state, it is very difficult to support users during a current crisis because it is very unlikely that they will use the application for entering a disastrous overeating day. It is also unlikely to experience an application’s relapse management support when testing an application only for a few days. Therefore, we evaluated applications’ relapse prevention support, but not relapse management and recovery support. We classified an application as providing relapse prevention support if it encouraged users to analyze current or past challenges in a positive way, provided support for mastering frequently encountered challenges, or supported restoring self-efficacy and motivation after a crisis. 9.2.2.13 Targeted Functional Domain In addition to HAPA concepts, we classified applications concerning the four QoLT dimensions (Schulz et al. 2012): functional domain, kind of functioning support, passive or active mode of operating the system, and system intelligence. As described earlier, we discriminated during filtering and evaluating applications between functional domains. At the broadest level, we discriminated between exercise and diet applications. Within those categories, we discriminated different sports and diet approaches. 9.2.2.14 Kind of Functioning Support By definition, most health and fitness applications address prevention and maintenance. We categorized applications regarding kind of functioning support based on aimed final performance. Running a 5K distance is within normal functioning and aims to maintain functioning. In contrast, running a marathon is out of range for most users and therefore enhancing normal functioning. Managing clinical conditions, like diabetes or allergies, compensates for diminished functioning. 9.2.2.15 Mode of Operating the System We analyzed mode of operation for instruction and tracking. Passive instruction does not consider user’s current performance for guidance. As an extreme case, instruction could

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be prerecorded on a tape. In contrast, interactive instruction would adjust guidance to current levels of performance like a good personal trainer. Passive tracking of current performance does not require users’ input, for example, GPS, pedometers, or sensors. In contrast, interactive tracking requires users’ input, for example, manual entry diaries.


9.2.2.16 System Intelligence We considered an application as globally intelligent if it assesses a user’s current performance level and health status during setup and adjusts the program correspondingly. For intelligent tutoring systems, this feature of curriculum development is referred to as the outer loop. We considered an application to be locally intelligent if it monitors current performance during a session and adjusts today’s program accordingly. For intelligent tutoring systems, this dimension is referred to as the inner loop.

9.3 Results We evaluated exercise applications in subcategories for tracking performance (N = 11; see Table 9.1a), running programs (N = 7; see Table 9.1b), and general workout programs (N = 23; see Table 9.2), including brief workout sessions (N = 3; see Table 9.2a), specific exercises (N = 4; see Table 9.2b), warm-up (N = 3; see Table 9.2c), diverse exercises (N = 3; see Table 9.2d), bodybuilding (N = 7; see Table 9.2d), and others (N = 2; see Table 9.2e). We evaluated diet applications (see Table 9.3) in subcategories for specific diet regimes (N = 6; see Table 9.3a), food databases (N = 5; see Table 9.3b), nutrition trackers (N = 7; see Table 9.3c), and comprehensive weight loss programs (N = 3; see Table 9.3d). 9.3.1 Exercise Performance Tracking We evaluated 11 applications that track performance during exercise (see Table 9.1a). Most advanced tracking applications originate from bike computers and have started to integrate tracking health measures along traditional athletic values. We included B.iCycle (Valley Development GmbH) and Road Bike Pro (Runtastic) as bike applications. For tracking walking performance, we analyzed Accupedo-Pro (Corusen LLC), SmartRunner Pro (APPSfactory), and Walkmeter (Abvio Inc.). For general workout and activity, we included Endomondo (Endomondo.com), Map My Fitness+ (MapMyFitness Inc.), MyFitnessCompanion (MyFitnessCompanion), Nexercise (Nexercise), and WOD Tracker Pro (Pete Wood). Finally, we included Heart Rate Pro (Runtastic) representing a line of development that aims to use mobile technology instead of additional sensors for measuring health parameters. Heart Rate Pro uses the iPhone camera for estimating resting heart rate. Exercise tracking applications mostly target advanced users during maintenance state and correspondingly provide detailed performance monitoring, but with very little instruction. As Table 9.1a shows, 8 of 11 applications assess demographics for estimating burned calories as one frequently displayed measure. Only two applications ask for users’ goals. For example, Accupedo-Pro asks during setup to set a daily goal and suggests 10,000 steps as default. If the interface is intuitive and performance does not require specific skills, like walking, applications can be also used for initiative state (N = 6). In tendency, tracking applications


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for beginners can be also used for action planning. Road Bike Pro provides trail information in advance including distance and difficulty level. No application communicates risks and resources to nonintenders or supports relapse prevention and management. Walking and biking can prevent decline of functioning. In contrast, bodybuilding aims to build capacity to lift weights beyond normal functioning. Bike computers and running trackers necessarily track performance during workout passively and accept manual entry for comments after workout. This is a prerequisite for local system intelligence that is still displayed by only a minority of health and fitness applications.


9.3.2 Running Programs Table 9.1b displays evaluation results for seven running program applications. All applications provide detailed instruction when to run and when to walk during each session. Applications vary in interface design and in tracking and analyzing performance capacities. 5K Runner (Clear Sky Apps LTD) and Get Running (Couch to 5K, Splendid Things) enable beginners during initiative state to run 5K in 8 or 9 weeks time. B210K Pro (Bridge to 10K, Guy Hoffman) and Run a 10K Pro! (Red Rocks Apps) target advanced users during maintenance state and aim for running 10K in 6 or 10 weeks time for enhancing normal functioning. Hal Higdon Novice1 26.2 Marathon (Bluefin Software, LLC) allows choosing a target distance between 1 and 42 km for an 18-week program. FIRST iPhone Companion (Bryan Catron) offers 12-week programs for 5K and 10K or a 16-week program for Half Marathon and Marathon. In contrast, Zombies, Run! (Six to Start) does not aim for a specific time or distance, but offers 30 or 60 min running sessions integrated in playing a game. Given their aim, all reviewed running program applications provide detailed instruction during a session. Beginners’ applications do not track distance or whether the user is running at all. This makes guidance very passive and not locally intelligent. In contrast, Higdon Novice1 26.2 and Run a 10K Pro! target advanced users and use GPS tracking. Zombies, Run! tracks distance, time, and pace during a session. All programs either allow to preview sessions for action planning or could be misused for planning by simulating sessions. None of the running program applications assesses users’ initial health parameters. Applications that provide several programs to choose from also ask for users’ target (FIRST and Higdon Novice1 26.2). Those two applications can also be used as race preparation tools and ask for the race day and name during setup. More than half of applications do not provide risk and resource communication. This is matched with missing coping support and absent relapse prevention for most applications. Interestingly, the long-distance applications are more likely to provide coping support. As for performance tracking applications, only minority of running program applications display global or local intelligence given missing assessment and passive, nonadaptive guidance. 9.3.3 General Workout Tools Table 9.2 shows evaluation results for 23 general workout applications. These are more diverse than performance tracking or running program applications and cover a variety of areas: Three applications (see Table 9.2a) are designed for brief full body workout sessions—7 Minute Workout (UOVO), Daily Workouts (Daily Workouts Apps, LLC), and Gorilla Workout (Heckr LLC). Four applications (see Table 9.2b) cover specific exercises for beginners: Burn Your Fat With ME!! (BYFWM!!, Creative Freaks) targets sit-ups and motivates users with a female Japanese cartoon character as personal trainer. Just 6 Weeks (Just Do Inc.) offers


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6-week programs for different exercises, including push-ups and sit-ups. Pushups 0 to 100 Exercise Workout Trainer (Clear Sky App LTD) and Runtastic Push-Ups Pro (Runtastic) provide programs aiming for 100 push-ups. Three applications (see Table 9.2c) represent a collection of warm-up and cooldown exercises or relaxation. 3D Tai Chi 8 + 16 Forms (ChuFengChin) offers a 3D simulated character that can be rotated to view any perspective during exercise demonstration. Daily Yoga (DailyYoga.com) provides yoga programs for different body parts, for example, back. Stretch Guru: Run (Lori Bryan Living) offers stretch instructions for different cooldown durations. Three applications (see Table 9.2d) have a collection of diverse exercises with diverse measures tracked and analyzed during workout. All-in Fitness (Fitness, Plus Sports) provides a variety of fitness and yoga exercises and passively tracks several measures during workout. HIIT—Interval Training Pro (Caynax) offers a high-intensity interval training program as cardiovascular workout for weight loss. Nike Training Club (Nike Inc.) is a workout guide including tracking and options for earning virtual rewards. Seven applications (see Table 9.2e) for advanced users on weight lifting or bodybuilding provide workout planners and trackers. The Bible of BodyBuilding (WorkoutRoutines) reads like a manual for bodybuilding with a 12-month workout guide. Crossfit WOD Programmer (CrossfitHardcore) supports workout planning and considers preferred equipment and preferred exercises. Gym Genius—Workout Tracker (AppBrains) offers six core weight lifting programs with comprehensive workout tracking. Gymme—Gym Personal Trainer (Matteo Pizzorni) and Gymprovise Gym Workout Tracker (Gymprovise) are exercise-planning tools. myWOD (theDreamWorkshop) presents WOD linked with YouTube instruction and benchmark models. Workout Hero (Storeboughtmilk LLC) is a weight lifting workout planner and tracker. Two reviewed applications (see Table 9.2f) stick out for different reasons. The PFA Calculator For U.S. Navy (Crash Test Dummy Limited, LLC) is a fitness assessment tool tailored for the U.S. navy applicant rating criteria. SuperBetter (SuperBetter Labs) is a resilience support game that almost looks like designed with HAPA in mind. It is not specific for exercise and weight loss, but can be used for those aims and includes tips and instructions on coping that is missing in majority of applications. Most general workout applications address either beginners during initiative state or advanced users during maintenance state. Beginners’ applications for brief workout or specific exercise programs mostly imitate running programs in their evaluation patterns: they provide passive instruction and action planning support. They also have very little initial assessment or risk communication. Only Daily Yoga mentions adverse effects of a sedative lifestyle as risk communication. As for running programs, there is a little more resource communication. Daily Yoga and Stretch Guru emphasize health benefits of exercise to support positive outcome expectancy. Fitness provides coping planning support by describing typical mistakes of beginners and how to master those challenges. As for running programs, beginners’ workout applications do not provide relapse prevention and management. Applications for brief workout sessions usually do not track performance as basic running programs do. In contrast, applications on specific exercise programs track repetitions. Most beginners’ workout applications do not display global or local intelligence as expected given missing assessment and passive instruction. Advanced weight lifting and gym workout applications target advanced users during maintenance state, but do not mimic distance tracking applications. They do provide guidance and some instructions are interactive: Gymme does not provide information on how to perform an exercise, but predicts how many repetitions are necessary for reaching personal



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bodybuilding goals given current performance. Gymprovise assesses in the beginning preferred exercises and equipment and suggests alternatives if needed. Most programs can be used for planning workout or they import WODs from RSS feeds eliminating the need to plan ahead (e.g., WOD programmer). All these applications track performance for displaying progress after assessing initial performance during setup. Advanced workout applications provide very little coping support or relapse prevention and focus exclusively on action planning and performance support. They mostly do not assess goals and provide no risk and resource communication. Predicting necessary workout for Gymme or compensating for occupied gym equipment in Gymprovise can be interpreted as indicators for system intelligence. While general workout can contribute to maintain functioning, advanced weight lifting and bodybuilding are more likely to enhance performance beyond normative functioning. There are two applications that did not fit either in the beginners’ applications or in the advanced athletes’ applications. U.S. Navy PFA Calculator is designed as a fitness assessment tool for one-time use. It asks for a couple of performance measures to be manually entered and calculates a corresponding pass/fail value. It does not track progress and does not provide instruction on how to get in shape for passing the test either. Therefore, U.S. Navy PFA Calculator misses by design most evaluation criteria. The other exception, SuperBetter, is also not a typical exercise application. It aims to increase users’ coping skills. Correspondingly, SuperBetter looks very well in our evaluation, but is not necessarily a very good exercise program. It tells users what to do if they do not have sufficient time to go to the gym. It does not tell what to do in the gym in case users have the time to go there. Similarly, tracking performance is much more important for biking or running than for yoga or Tai Chi. Therefore, missing tracking would be a disadvantage for advanced running programs, but not for yoga programs. This demonstrates the importance of the QoLT dimension of targeted functional domain when evaluating mobile health and fitness applications. 9.3.4 Diet Tools Table 9.3 presents evaluation results for 21 diet applications with great overlap of functionality. Six of these applications (see Table 9.3a) advertise a specific diet program for a limited period of time: 3 Day Military Diet (Realized Mobile LLC) suggests keeping a diet regime for 3 days followed by 2 days off each. 17 Day Diet Complete (Realized Mobile LLC) proposes a 51-day program for weight loss with three stages. Fast Metabolism Diet App … (Random House Digital Inc.) presents a 28-day diet without counting nutrition values. The Monash University Low FODMAP Diet (CEMA, Monash University) aims to assist in management of irritable bowel syndrome (IBS) by restricting some carbohydrates for a weeklong challenge with symptom tracking. The hCG Diet App (Appilicious) supports hCG treatment for weight loss with a strict eating regime complementing the hormone therapy. The Fit Chick Meal Planner & Recipe Sharer (Brett Campbell) comes with a 7-day clean eating meal plan. Five applications (see Table 9.3b) support healthy eating choices by providing a food database with essential nutrition information: Eat This, Not That! Restaurants (Rodale Inc.) provides best and worst options for each restaurant. Green Smoothies (Raw Family) presents a recipe collection including nutrition information. Healthy Food, Allergens, GMOs & Nutrition Scanner (NxtNutrio, Nxtranet Inc.) analyzes food ingredients and helps spotting allergens. Paleo Central (Nerd Fitness) classifies food items as either adhering the Paleo


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philosophy or not. Points Calculator for Weight Loss … (iTrackBites, Ellisapps Inc.) supports tracking of food score values. Seven applications (see Table 9.3c) support logging over time calories, body weight, and sometimes exercise: CaloryGuard Pro (BlueBamboo) is a diet and exercise tracker. Carb Master—Daily Carbohydrate Tracker (Deltaworks) and CarbsControl—Carb Counter and Tracker (Coheso Inc.) are diet trackers supporting low-carb diets or diabetes management. Cronometer (BigCrunch Consulting Ltd.) is an online diet and exercise tracker. DietBet— Loose Weight. Make Money (DietBet) is a social weight loss game that rewards players who lost 4% of their body weight in 4 weeks on cost of players who did not. Nutrition Menu (Shroomies) tracks several nutrition and exercise measures and provides nutrition information based on a food database. Nutritionist (OTK) is a nutrition and exercise tracker with meal planning. Finally, three comprehensive weight loss programs (see Table 9.3d) integrate meal planning, nutrition information, and tracking. Bodybuilding Diet Pro (Zen Software, LLC) provides a long-term program for building muscle. My Diet Coach—Weight Loss for Women (Inspired Apps) visualizes users’ weight loss goal with before-/after-animated characters and provides a variety of tips for coping with frequent challenges. Noom Weight Loss Coach (Noom Inc.) is the most comprehensive weight loss program in our selection and offers a variety of weight loss support tools. By definition, weight loss applications address mainly users in the action planning and initiative state (N = 20 of 21), and weight management applications address mainly users during maintenance state (N = 10) with partial overlapping. While DietBet and explicit diet trackers provide no instruction, all other applications provide passive information. It is worth noting that database tools provide information, but no or very little guidance. In contrast, weight loss and management programs provide guidance and action planning support as well. As for exercise applications, majority does not address relapse prevention or management, but more applications provide basic coping support. My Diet Coach presents a collection of typical challenges and how to master these and has a food cravings panic button. Nutritionist explains the basics of dieting, including recommendations to restructure the environment for preparing for challenges and resisting cravings. Eat This! suggests the healthiest option even for fastfood restaurants allowing to manage a crisis. While it is very easy for distance tracking tools to passively monitor performance, passive nutrition tracking is challenging for diet applications. Entering breakfast can easily take 15 min for the first time with significantly less effort for repetitions. A nice exception is Noom with an option for fast versus accurate tracking. The tracking challenge also becomes easier for barcode scanners and restaurant trackers that track complete meals. The challenge disappears for diet programs that do not believe in calorie tracking, like Paleo or Fast Metabolism Diet, or programs that prescribe a menu, like Fit Chick. For weight loss programs, current and target weights are usually assessed during setup (e.g., My Diet Coach or Noom). About half of the application communicates health risks and/or expected benefits. This holds especially for applications that support management of clinical conditions, like Low Fodmap Diet and NxtNutrio. In general, weight loss and weight management programs aim to prevent decline of functioning, while management of clinical conditions (Carb Master, Carbcontrol, Low Fodmap Diet, NxtNutrio) aims to compensate for diminished functioning. As for exercise applications, global and local intelligence is still rare among diet applications resulting in passive instruction and missing advanced coping and recovery support.


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9.4 Discussion There are currently huge numbers of mobile health and fitness applications on the market. We started our evaluation project with 2331 applications from iTunes and Google Play stores. These applications offer a great variety of features and variance for implementing the same functions. Therefore, we presented our evaluation results for only 61 applications covering fast majority of features from the originally 435 after filtering. When filtering by functionality, we necessarily introduced bias toward prevention applications targeting health behavior of users. At the same time, our evaluation criteria would not have done justice to some excellent patient management applications. Our evaluation of covered HAPA components revealed that majority of mobile health and fitness applications are designed for a specific state of behavior change state, but do not assess whether potential users are currently in that state or support users to reach that state. Still, some running programs provide siblings of applications that cover different distances and correspondingly cover action planning, initiative, and action maintenance states. This specialization toward a specific BCS shows best when discriminating between beginners’ (or action initiative state) applications and experts’ (or action maintenance state) applications that exist for all subcategories. In tendency, applications instruct beginners and track experts. Most applications have in common that they provide much more action support than coping and relapse prevention support, especially for exercise applications. However, there are systematic variations between and within categories: for all categories, there are implementations of very specific support, like bike computers and food databases, but also all-around talents, like general fitness applications or comprehensive weight loss programs. In tendency, we have seen a greater overlap in functions between subcategories among diet applications than among exercise applications. There is also in tendency more coping support within diet applications than within exercise applications. This seems intuitive given frequently reported food cravings, but not workout cravings. Still, exercise applications would greatly profit from guidance for difficult users with clinical conditions or for injury management. For both exercise and diet applications, there are promising exceptions from the rule providing extensive coping support via games, bets, challenges, social support, or virtual love. From our evaluation of implemented QoLT dimensions, we took, first, the discrimination between functional domains that highlighted differences and similarities between and within categories. It is important to note that our discrimination of functional domains came from a perspective of functions covered by applications. A patient looking for support while recovering from heart attack might have drawn the lines differently. Given that focus, our selection of mobile health and fitness applications mostly covered the segment of preventing decline of functioning than compensating for diminished functioning or enhancing normal functioning. As for the HAPA components, most applications cover a specific segment along the continuum of functioning support. That matches Schulz and colleagues’ (2012) observation for QoLT systems in general. Also matching Schulz and colleagues’ analysis of current QoLT systems, majority of mobile health and fitness applications displays very little system intelligence. Again, there are a couple of basic versions of global system intelligence, like adjusting calories burned during exercise to a user’s BMI, and a few promising exceptions. For example, Bodybuilding Diet suggests considering an 85+-week program for competing in a bodybuilding competition for a desperate male user with 200 lb body weight and low activity

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level. Related to low system intelligence, most applications provide passive and not-interactive guidance. Since the advent of GPS tracking, barcode scanners, and sensors integrated in mobile phones, tracking is increasingly passive for monitoring performance. But there remains a challenge to track meals that do not come out of a box or from a restaurant. Taken together, we found that HAPA theory components and QoLT evaluation criteria nicely complement each other. Without assessment of states as suggested by HAPA, there would be no option for system intelligence. Without adapting to users’ preferences and needs, an application cannot provide meaningful coping and relapse support.

9.5 Limitations The evaluation of mobile health and fitness applications that we have conducted has several limitations. First, we have developed and applied an evaluation schema based on the HAPA theory and QoLT dimensions. Within our workgroup, we have reached a reasonable inter-rater reliability. However, this schema is not fully tested yet and might need further modifications for being sufficiently reliable and valid for evaluating mHealth applications on a regular basis. Second, here, we applied this schema to diet and exercise applications, but not to other Health & Fitness applications in iTunes and Google Play stores. For some of those, for example, smoking cessation, HAPA has been successfully used for modeling behavior change and designing interventions (e.g., Ochsner et al. 2014). However, in our sample, we had only 12 smoking cessation applications. Therefore, we would not have fairly evaluated those compared to the huge collection of diet and exercise applications. We also did not evaluate patient management applications or sports applications because they either do not implement stand-alone applications or do not aim to reduce health risks. Third, we sampled the U.S. market and partially the German market for mobile health and fitness applications, but not other regions. We also did not explicitly address cultural issues (see, e.g., Limam Mansar et al. [2014] for an adaptation of weight loss applications to the Middle East). Fourth, this systematic review mirrors the state of iTunes and Google Play stores Health & Fitness applications in June 2013. Some of the reviewed applications might have been updated since that time or have disappeared from the stores. Fifth, to our knowledge, there does not exist evidence yet on mobile applications that implement HAPA and QoLT dimensions effectively improving users’ health and wellbeing. This taps into the issues of life cycles for mobile applications that are much shorter than the average duration of clinical trials. However, clustering applications according to HAPA and QoLT dimensions and conducting clinical trials on groups of applications instead of single implementations might help to bypass this limitation of clinical trial methodology. Finally, this schema is designed for experts who design and evaluate mHealth applications. It is not intended for users of health applications. The complexity of health, interventions, and mobile technology is too high to be easily understood by laypeople and applied to their own life. Potentially, this schema can be developed into a rating system similar to the five-star popularity system for displaying compliance with evidence-based recommendations for efficient and user-friendly mHealth applications.


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9.6 Case Study The operational definitions described in the methods section can be also used for designing mHealth applications. For designing a mobile application to complement a local weight loss program, we (Limam Mansar et al. 2012) first defined the intended QoLT dimensions. The application was designed as add-on for managing health behavior goals, motivational support, and social networking integrated in a weight loss program at a local health clinic. Therefore, it primarily provided cognitive support in semiautomated mode of operation with a basic version of system intelligence. According to HAPA, intended users had already passed intention state. During the 4-week program, users were expected to pass through preparation and action initiative state with in-person support for planning and application-based support for action initiative. During setup, the application asked users to enter personal goals for the coming week and to choose frequency and time of reminders and text messages. During a week, the application provided detailed instruction on performance and feedback on achieved goals supporting users’ action and coping self-efficacy (for more details, see Limam Mansar et al. 2012). This is an example for very limited add-on support for users during planning and action initiative state.

9.7 Future Directions For future development, currently available mobile health and fitness applications demonstrate the departure from clinical patient management programs on one side and from athletic expert systems on the other side and start to approach the area of preventive medicine for fostering citizens’ health behavior concerning exercise and diet. Current mobile applications provide more action support than coping support and miss the capacity of required system intelligence to adapt to user’s needs and challenges. They do well for young users without medical conditions, but they are not well equipped to serve difficult users, for example, a patient recovering from heart attack or a patient trying to stabilize his or her blood glucose readings. Current applications also started to connect diet with exercise and integrated tracking health measures for both areas, but they are still stronger on one side than the other. Comprehensive weight loss and management applications should excel on both for increasing usability, user satisfaction, and effectiveness.

9.8 Conclusions For improving current mobile health and fitness applications, the greatest challenges are not more advanced sensors. The greatest challenge is to provide adaptive and intelligent guidance for good and bad times. It is naïve to assume that a couch potato masters a 5K program successfully from day 1 on. Therefore, it is not good enough to tell that person when to walk and when to run. It is necessary to track whether he or she is actually running and providing feedback and motivational support accordingly. There is a need for current mobile health and fitness to become more intelligent to provide users with what they are looking for.

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Case Scenario Mrs. Smith has been told by her family doctor to significantly lose weight after receiving test results indicating prediabetic status. Mrs. Smith signals willingness, but she needs guidance. The doctor recommends trying Fit!, a new weight loss application developed under guidance of the local teaching hospital. When opening the application first time, Mrs. Smith is welcomed, is asked a few questions on demographics, and is prompted to choose a fitness goal from a list. After that, the application updates for a moment and invites her to explore different features. All starts very easy with taking a picture of her meals first day and just carrying around the phone in her pocket. During the next days, things become more demanding with signaling that she has reached her daily calorie budget already before lunch. The application suggests walking in the park for half an hour, but it is raining heavily outside. After trying hard for 2 weeks, Mrs. Smith feels exhausted. The application is bossing her around and leaves very little wiggle space. When a coworker invites her for an ice cream, she cannot resist. Mentally counting calories, she switches off her phone and does not turn it on until recovering from her blackout 2 days later. She feels ashamed and asks herself whether she is the only weak person on the planet. Her cell phone says “Great job! You sticked to the budget for a week in a row.”

Questions

1. According to HAPA, in which state is Mrs. Smith in the end of this episode? 2. Given the description, which kinds of assessment did the Fit! application implement? 3. Given the description, which kind of intervention did the Fit! application provide? 4. Which kinds of self-efficacy did Mrs. Smith display during her first week of diet? 5. Which modes of operating the system did the Fit! application offer according to QoLT evaluation dimensions? 6. What do you think: Does the Fit! application display system intelligence?

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