Personalization in Real-Time Physical Activity Coaching using Mobile Applications: A Scoping Review

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 1 Abstract— Mobile monitoring for health and wellness is becoming more sophisticated and accurate, with an increased use of realtime personalization technologies that may improve the effectiveness of physical activity coaching systems. This study aimed to review real-time physical activity coaching applications that make use of personalization mechanisms. A scoping review, using the PRISMA-ScR checklist, was conducted on the literature published from July 2007 to July 2018. A data extraction tool was developed to analyze the systems on general characteristics, personalization, design foundations (behavior change and gamification) and evaluation methods. 28 papers describing 17 different mobile applications were included. The most used personalization concepts were Feedback (17/17), Goal Setting (15/17), User Targeting (9/17) and Inter-human Interaction (8/17), while the less commonly covered were Self-Learning (4/17), Context Awareness (3/17) and Adaptation (2/17). Few systems considered behavior change theories for design (6/17). A total of 42 instances of gamification-related elements were found across 15 systems, but only 6 explicitly mention its use. Most systems (15/17) were submitted to some type of evaluation. However, few assessed the effects of particular strategies or overall system effectiveness using randomized experimental designs (5/17). Although personalization is thought to improve user adherence in physical activity coaching applications, it is still far from reaching its full potential. We believe that future work should consider the theory and suggestions reported in prior work; leverage the needs of the target users for personalization; include behavior change foundations and explore gamification theory; and properly evaluate these systems. Index Terms— mobile applications, mobile health, personalization, physical activity, persuasive technology, scoping review, tailoring. I. INTRODUCTION It is well understood that we need innovative approaches to address the alarmingly low levels of engagement in physical activity (PA) among the general population. The use of wearable and mobile monitoring technologies for this purpose has exploded from a standing start in the last 10-12 years. In this line, there has been important progress on the use of these systems to increase adherence to PA [1], both for the healthy population as well as for prevention and management of chronic diseases [2]–[4]. In particular, PA coaching applications are Paper submitted on 21st February 2019. This work was supported by the European Union’s Horizon 2020 research and innovation programme: Marie Sklodowska-Curie Actions grant agreement no. 722012. The publication of this article was funded by the Qatar National Library. F. Monteiro-Guerra is with Salumedia Tecnologías, Seville, Spain and The Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland (e-mail: [email protected] or [email protected]). defined as systems that aim to motivate the user to change their activity behaviour by means of a coaching element [5]. A common motivational strategy used in PA coaching is Feedback, which is a way to stimulate such change by generating awareness of the user current behaviour. Despite an increase in number, complexity and accuracy, these systems face the underlying challenge of user abandonment, which has been highlighted in recent publications [6]–[8]. Studies report that users may stop using such technologies once they have gathered enough information about their routine activities [8], [9]. Also, when it comes to commercially available solutions, these often target young and active people [10], who do not require special recommendations nor motivation. For users who need to be persuaded to become active, effects seem promising in the short-term but users do not feel additional inducement to use the devices [10], [11]. In order for these persuasive technologies [12][13], to make an impact on user’s behavior, researchers have highlighted the importance of including a strong theoretical basis considering different aspects of behavior change. In particular, a metaanalysis from Fanning et al. shows that mobile-based PA interventions tend to be more effective when relying on behavior change theories (BCTs) and models [14]. Some of the most used are the Social Cognitive Theory [15], the Transtheoretical Model [16] and the Self-Determination Theory [17]. Yet, relatively few health apps explicitly rely on these theories [18]–[21]. Also, as brought up by Fogg et al. in [12], for the behavior change program to have a sustainable impact, it is essential that automated systems engage people. In that context, gamification, defined as “the use of game design elements in nongame contexts” [22], has recently emerged in the design of persuasive health technologies [23], [24] with particular uses in health and fitness apps [25]. The field has been on a rapid rise [26], with evidence suggesting its potential in creating pleasant experiences for the users of technology. Overall, the high levels of user abandonment have been commonly associated with a low perceived personal relevance and a lack of engagement [11], [27], which highlights the importance of exploring additional motivators to adopt sustainable healthy behaviors. A key factor that may determine persuasiveness to behavior change is related to creating personalized, or tailored, experiences to each individual [28]. O. Rivera-Romero is with Universidad de Sevilla, Seville, Spain (e-mail: [email protected]). L. Fernandez-Luque is with the Qatar Computing Research Institute, Doha, Qatar (e-mail: [email protected]). B. Caulfield is with The Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland (e-mail: [email protected]). Personalization in Real-Time Physical Activity Coaching using Mobile Applications: A Scoping Review F. Monteiro-Guerra, O. Rivera-Romero, L. Fernandez-Luque, and B. Caulfield This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 2 As stated in [10], each individual is unique, and dynamic, in a sense that a strategy that works for one, might not work for another. It is believed that mobile-based interventions that are closely tailored to the individual’s convictions and motivations are more likely to be observed and remembered [29]. Therefore personalization, or tailoring, helps in increasing the intended effects of communication, which can contribute to overcome the lack of adherence and effectiveness of these systems [30]. Furthermore, with mobile technologies allowing for more accurate, usable and engaging real-time support, there is an increase in alternative forms of personalization that can potentially make a difference in the effectiveness of PA coaching applications. Hawkins et al. in [30] defined tailoring as “any of a number of methods for creating communications individualized for their receivers...”. Since 2008 a number of papers were published related to tailoring technology-based health interventions [31]–[33]. The most recent work in this area, by op den Akker et al., deals specifically with real-time tailoring of PA coaching applications [5]. The authors report on a literature survey, with data collected until July 2013, and define a tailoring model relying on 7 different concepts: Feedback (FB) - presenting the measured amount of activity performed to the user (can vary in timing, content and representation); User Targeting (UT) - conveying that communication is designed specifically for the user; Goal Setting (GS) - creating and updating user-specific goals based on users’ activity trends and patterns; Inter-human Interaction (IHI) - providing support by form of interaction with other humans; Adaptation (Ad) - directing information to individual’s status on key behavioral factors; Context Awareness (CA) - using users’ external context to provide relevant information; and Self Learning (SL) - learning reactions of the users’ to previous communications. However, the authors highlighted the lack of systems exploring the full potential of smartphones and available contextual information for the design of more complex personalization; the lack in application of Ad, CA and SL; the lack of clear specification of theoretical foundation for specific design decisions; and the lack of work demonstrating the effectiveness of tailoring in a more structured and controlled manner. The exponential increase in evidence related to mobile PA coaching technologies motivated this scoping review. The aim is to systematically map the most recent developments on techniques used in these real-time systems that aim to motivate users in reaching their personal activity related goals. The specific objectives are to: i) expand the knowledge on personalization in real-time PA coaching applications, by presenting current advances in the field, ii) understand if previously reported gaps have been addressed and identify opportunities for future work, and iii) to provide a comprehensive analysis of these applications considering general system characteristics, behavior change theoretical foundation, use of gamification and system evaluation. Due to the scope of this review, it is clear that it does not cover all the work done on tailoring/personalization nor on physical activity coaching systems. Instead it explores a narrow topic that sits inbetween these two fields. II. METHODS A methodological scoping review [34], [35] was conducted to study real-time personalization in PA coaching mobile applications, and was built upon a prior literature survey and model published by op den Akker et al. in 2014 [5]. The protocol was drafted using the PRISMA extension for scoping reviews (PRISMA-ScR) checklist and explanation [36]. This extension provides reporting guidance for this specific type of knowledge synthesis. The checklist contains 20 essential reporting items and 2 optional items, which detail how to conduct and report scoping reviews. This article does not aim to derive statistical evidence or conclusions from existing literature, as this is not applicable for scoping reviews. A. Search Approach The search strategy, used to identify potentially relevant studies, was based on [5]. Two searches were performed, one on July 12th, 2017 and another, to update the previous results, on July 10th, 2018. For the first search, 7 databases were selected as the source of information: PubMed; Association for Computing Machinery (ACM); ScienceDirect; IEEEexplore; PsycINFO; CINAHL; UCDlibrary-onesearch. A second search was conducted in PubMed, ACM and IEEEexplore, to include relevant papers published since the first search (from July 12th, 2017 to July 10th, 2018). These 3 databases were used as they covered all the selected studies from the first iteration. The search strategy was based on the previous review in this specific topic [5], and was conducted as follows: (personalized OR personalised OR personalization OR personalisation OR individualized OR individualised OR individualization OR individualisation OR tailored OR tailoring) AND ("physical activity” OR "daily activity" OR walking OR exercise OR exercising OR "activities of daily living”) AND (coach OR coaching OR feedback OR motivate OR motivation OR stimulate OR stimulation OR promote OR promotion) AND ( app OR application OR system OR device). When offered the option, keywords were searched in the entire text of the article. To be included, papers needed to: be written in English; be published in conferences or journals over the last 10 years (from January 1st, 2007 to July 12th, 2017, when the first search was performed); deal with PA coaching systems, including either promotion of daily activities (e.g.: walking, running), in-session coaching, or prevention of sedentary behavior; describe systems with some kind of personalization to the user; describe real-time coaching systems; describe systems that are smartphone-based and make use of embedded and/or external sensors to measure PA. Studies were excluded if they were: exergame-based; targeted at disease rehabilitation; targeted at specific exercises (e.g.: rehabilitation exercises or machine exercises); with no direct connection between sensor and smartphone (e.g.: systems in which a server is an intermediary of data synchronization between sensor and smartphone were excluded); with no real-time communication with the user. Also, papers were excluded if the full paper was not available. The real-time definition used in this study was the same considered in [5]. Following this definition, a real-time system is one that has a direct connection between sensor and feedback device, and that is able to communicate constantly with the user This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 3 and provide immediate feedback on measured performance. For such real-time purposes, the feedback device could be either the sensor itself or a mobile phone/smartphone. The focus was on the latter modality as it offers more opportunities for richer processing and visual display. Some systems included in the previous review [5] were found through manual search, which means they could be missed in our database search. In such case, these systems were also included for analysis. Additionally, the research team searched for other papers with further system details by scanning the reference list from the included papers and through manual search in google scholar. The manual search terms included the author names and the system name. B. Study Selection Procedure The study selection procedure was divided into four phases. The identification phase consisted of retrieving articles from the databases and was performed by one researcher (FMG). After removing the duplicates, resulting papers went through the screening phase, first by title and then by abstract, by two researchers (FMG and ORR). In case of doubt, the paper was included and reviewed again in later stages for final inclusion. Then, a full-paper review was conducted by the two researchers (FMG and ORR) for eligibility, considering the defined inclusion/exclusion criteria. The included papers were then assessed for data extraction. Discrepancies on study selection and data extraction were solved by consensus. Cohen kappa coefficient was calculated, to measure inter rater agreement, in the title review (k = 0.734 on a random sample of 38 papers) and in the abstract review (k = 0.85 on a random sample of 100 papers) [37], revealing a substantial agreement between the two reviewers. C. Data Extraction A data-charting form was constructed by two researchers (FMG and ORR) based on the suggested in [36]. Following an iterative process, the authors identified the most relevant categories related to the objectives of this study and they were included or updated in the form. Discrepancies were solved by consensus. The data was abstracted regarding 4 main categories. The first one included general system details on: target population, target activity, main features, inclusion of human (coach)-in- the-loop, platform used and market availability. The second aspect addressed was personalization, which was based on the model and framework provided in [5], and covered: coaching mode (if the communication is provided during daily activities, during exercise sessions or during sedentary behaviors), personalization concepts and mechanisms, technical implementation, communication properties addressed and inclusion of user profiling. The third category regarded the persuasive design considerations, and addressed the use of theoretical foundation and the inclusion of gamification elements [23]. The fourth, and last, category was on evaluation methods and included information on: study design (based on the framework in [38]), study population, study/intervention description, outcome measures and persuasive strategies comparison. Theoretical foundation was only extracted if theories were explicitly stated in the paper. The presence of gamification elements was extrapolated for all systems based on [23]. The systems were, however, distinguished based on explicit or implicit use of gamification. Explicit use was considered in papers that included gamification-related terms in the system description (e.g.: “games”, “game-based”, “game-like”, “gamification”,...), while implicit use was attributed to those systems that include gamification-related elements without explicitly reporting to do so. The details of the data-charting form for each of the included systems were independently extracted by two researchers (FMG, ORR). After data extraction, classification discrepancies were resolved by mutual agreement. D. Data Analysis Two researchers (FMG and ORR) went through the taxonomy table for all the included studies, in an attempt to find relevant insights to the research objectives posed for this review. Common patterns, contradictory results, and gaps were also analyzed for all studies. III. RESULTS We retrieved 1274 results from the first database search from which 341 were duplicates. To the remaining results, 200 new papers (without duplicates found) were added from the second database search, summing a total of 1133 results for screening. 555 met the title review criteria and, from these, 154 met the inclusion criteria in the abstract review. From the full-text review, 18 out of the 138 were then selected for inclusion, to which we added 8 more papers with further description of the included systems and 2 more with a system included in the review of op den Akker et al. In total, we included 28 papers [39]-[66] that covered 17 different PA coaching applications that explore personalization strategies in their design. A flow diagram representing the full process is shown in Fig. 1. Fig. 1. Flow diagram of the search strategy. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 4 16 out of the 28 included papers (some describing the same system) were new compared to the previous review, contributing with 10 new apps and 1 updated version of an already reported system. 5 of the systems found by their team were not retrieved in our database search, as these were probably identified through the manual search they have performed in Google Scholar and their personal libraries. From those 5 systems, we only included 1 (with 2 associate papers), which respected our criteria for being a mobile phone app. Therefore, a total of 17 systems were included in our study, and the analysis is presented in the following sections of this paper. A. General System Characteristics An overview of the systems’ details is provided in Table I and includes a descriptive summary of the systems’ objectives, main features, platform and market availability. TABLE I GENERAL SYSTEM CHARACTERISTICS a Systems also covered in the literature survey by op den Akker et al. in 2014 [5]. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 5 App screenshots of some of the included systems are presented in Fig. 2. B. Personalization The personalization concepts implemented in each app are discriminated in Table II, where the systems were categorized according to the coaching mode: over daily life activities (12 apps); during exercise sessions (3 apps); or to reduce sedentary behavior (2 apps). The most frequent used concepts were Feedback (used in all 17 apps), Goal Setting (15 apps), User Targeting (9 apps) and Inter-human Interaction (8 apps). The less frequent were Self Learning (4 apps), Context Awareness (3 apps) and Adaptation (2 apps). Move2Play and INTELiRun covered the highest number of personalization concepts, both with 6 instances, followed by Sweetch and AAFS with 5 instances. The following subsections describe the different personalization concepts and mechanisms used by the systems. 1) Feedback Feedback (FB) is the most obvious form of personalization and is used by all of the included apps. Considering the intention of the FB, we can separate the included systems in two main categories, those that aim at promoting PA (daily activities or exercise) and those that aim at reducing sedentary behavior (SitCoach and B-Mobile). On11 and Analytic, Social, Affect feature both intentions. They monitor sedentary behavior and try to reduce it, but also monitor active periods and provide coaching to achieve certain activity goals. In terms of timing, FB initiative can be with the user, when information is provided only if the user looks at a glanceable display (e.g.: UbiFit Garden, BeWell), or with the system, when it provides cues for the user to walk/run faster or slower based on user’s speed (e.g.: Haptic Personal Trainer, u4fit). Another aspect of timing relates to the type of coaching, which can be during daily activities or during exercise sessions. Considering the content of FB, only 2 papers seemed to address this property, reporting specifically on the phrasing of FB. The authors from the paper on the AAFS system, tested three types of messages: encouraging, neutral and discouraging. Also, in the SitCoach study, the authors tested motivational messages phrased using 4 different persuasive strategies based on the social influence theory. Regarding FB representation, the simplest form is through text messages and/or text notifications. Most systems also make use of some kind of visual feedback, either through graphs or more complex visual displays such as avatars (e.g.: Move2Play) or virtual ecosystems that change based on the user state (e.g.: UbiFit Garden and BeWell). Audio feedback was explored in 3 systems, TripleBeat, Haptic Personal Trainer and SitCoach. These last two also explored tactile cues as a mechanism for providing feedback. 2) Goal Setting Goal setting (GS) is used by most of the included applications (15 out of 17) and is a strategy commonly associated with FB. Many of the applications, in particular the ones that provide coaching during life activities, include simple daily and/or weekly numerical goals such as number of steps, distance or activity duration. On the other hand, applications that provide coaching during a workout session can provide goals in the form of session routines that the user can follow step-by-step and that can be based on the type of activity, pace, duration, or time spent in a specific heart-rate range (e.g.: TripleBeat and u4fit). Some systems allow the user to choose from a set of highlevel goals and then present more specific objectives or suggestions according to that goal choice. TripleBeat proposes a workout schedule based on a general goal, for example, to lose fewer calories but burn more fat, or to improve cardiovascular/ respiratory health. On11 allows the users to select from three types of goals: Keep Healthy, Lose Weight, or Burn Calories, and then suggests appropriate activities to help them achieve those goals. Regarding the creation of overall activity plans/calendars, only a few systems seem to implement this feature (e.g.: CAMMInA and u4fit). In u4fit, for example, the schedule is composed of several sessions per week for several weeks. In general, most systems let the user set or review their goals. Only u4fit and AAFS allow the training goals or plans to be set or adapted by a professional (human-in-the-loop). Several systems consider the user’s characteristics (e.g.: age, weight, preferences, physical activity level) in the definition of goals, combining GS and UT. iBurnCalorie provides a daily personalized caloric estimate that the user needs to expend, based on personal information provided during the registration process and daily requests. StepbyStep takes into account the user's baseline level of walking, then automatically sets a daily walking goal reflecting a 10% increase over the baseline level. The personal trainers from the u4fit system use information from the user’s physical activity profile and his progress to adapt the plan provided. In the AAFS system, the goals are set based on a baseline of activity for each day of the week and are kept up to date based on the daily progress and the activity pattern throughout the day. Move2Play recommends the appropriate amount of activity based on characteristics present in user and domain models. Also, the Sweetch app continuously adapts goals based on the user's real-world behavior and weight. BeWell+ is a particular case that uses community adaptive GS, adapting the goals based on the performance of the users compared to other similar users that perform slightly better. In terms of representation of goals, most systems are making use of visual displays like progress bars, goal lines in graphs (see systems figures), or cues in virtual ecosystems, rather than relying solely on textual format. 3) User Targeting A variety of forms of User Targeting (UT) are used by 9 of the included applications. The most transparent way of implementing UT is implemented by Move2Play, SitCoach, Sweetch and iBurnCalorie apps, which include the users’ names or nicknames in the main screen and/or in the textual feedback. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 6 Other less transparent approaches involve adapting the information based on the user characteristics (e.g.: age, weight, preferences, physical activity level). As described previously, this is mostly used in pair with GS strategies: the iBurnCalorie makes use of personal information provided during the registration process (age, gender, height, and weight during the registration process) and daily estimates of the users’ mean caloric food intake per day; StepbyStep takes into account the user's baseline level of walking; u4fit system uses information from the user’s physical activity profile and his progress; the AAFS system, uses the users’ baseline of activity, daily progress and activity pattern throughout the day; Move2Play system adapts to the characteristics present in user and domain models (e.g. age, gender, physical activity fitness); the Sweetch app makes use of the user's real-world habits and weight data; and INTELiRun incorporates age, height, weight, heart rate and injury history. On11 is a system that combines UT with FB by providing estimations of burnt calories to the users based on personal information from the user profile interface (gender, age, height, and weight). A less reported and detailed strategy of UT is to consider the users’ preferences to provide personalized suggestions or recommendations. For example, Move2Play recommends the appropriate type activity, based on the user activity preferences, in a way that will lead to the fulfilment of the daily plan and that the user will enjoy. Also, On11 provides walking suggestions and recommends detours based on personal preferences. 4) Inter-human Interaction Inter-human Interaction (IHI) is used in 8 of the included systems and can also be covered through a variety of different strategies. Social comparison is the most common form of IHI. The iBurnCalorie app makes use of a trending graph at the bottom of the home screen that provides an overview of the user’s status (activity trend or driving trend) compared to the social trend. Also, Move2Play and StepbyStep include a feature that ranks users in a leaderboard according to their achieved results. Move2Play tries to ensure a fair competition by considering the relative effort from the users, based on their fitness and physical condition. A more complex strategy is implemented by TripleBeat, where the user can compete with other virtual or real users, or the actual user on past runs. This competition is defined by how well users achieve their predefined goals, not by who is faster or exercises longer, aiming to promote healthy goal achievement. In the paper, the authors detail the strategies used for this purpose and to create a fair competition, which are based on a novel performance score function and similar opponent selection. BeWell+ app implements social comparison but implicitly, in combination with GS and FB, by comparing the performance of individual users with other peers, but in this case with users that are similar in behavior and that perform slightly better (through similarity matching). Some systems incite teamwork among the users. Such can be done either through group-based competition, as done in Move2play and the social version of the Analytic, Social, Affect system, or by simply allowing the users to create shared goals with others, which is also provided as an option in Move2Play app. Another form of IHI relies on enabling sharing of results and providing support among a network of users. The social version of the Analytic, Social, Affect system includes an electronic message board where participants can post comments or suggestions to the other participants. In Move2Play users can connect with friends, see their progress and compare their results, and this is made easier with the integration with popular social networks. The u4fit application allows users to create, save, and share their workout session results on Facebook. INTELiRun also allows users to share their running data on multiple social media platforms, to send and receive challenges from others and to find similar fitness running mates (via a matching feature in the app). A less frequent strategy of IHI is to include interactions with a human coach, which is one of u4fit’s main features. In u4fit a coach (chosen by the user) creates a tailored workout plan, analyses the training data gathered from the app and has the option of modifying the plan and motivating the user by means of the internal messaging system. Fig. 2. Example screenshots of 4 real-time PA coaching applications. a) StepByStep [54], b) iBurnCalorie [56], c) u4fit [62] and d) B-Mobile [64]. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 7 5) Adaptation Only 2 system covers the concept of Adaptation (Ad) as a personalization strategy. The AAFS combines Ad with FB, by providing tailored messages based on the user's score on self-efficacy and stage of change questionnaires and also on the user's own baseline level of physical activity (UT). With that information users are identified as one of 8 personas and recommended one of six feedback strategies. The INTELiRun app also prompts the user with a personality questionnaire, and provides feedback specific to each personality type. 6) Context Awareness Context Awareness (CA) is used in 3 out of the 17 included systems. However, the papers do not provide much details on its use and implementation, except for the one describing On11. In Move2Play, they reported on a PA recommendation algorithm that takes into account a Domain Model, which holds stable facts/knowledge about how we generally exercise and what factors affect the amount of activity. The model integrates factors such as day of the week, the month or season and the weather. On11 encourages users to walk by adding detours into their usual travel routes, such as home-workplace route or routes to their frequently visited destinations, and suggesting mini-walks around their workplace such as walking to the coffee lounge. The implementation of the recommendation system is detailed in the paper and takes into consideration the user's current location, location history, date and time. The Sweetch app is context aware in the sense that it notifies the user to do activity only when the user's calendar indicates available time and recommends specific activities based on the user's surrounding locations. 7) Self Learning Self Learning (SL) is covered in 4 of the included systems. The BeWell+ system uses SL in the way it implements the similarity matching algorithm for GS and FB, by repeating the matching process as new behavior data from the user is available. In that way, the system will incrementally set more challenging goals to the user, by selecting as frame of reference higher performing people that are still relatively similar to the user. Move2Play includes a user model (containing physical fitness, activity patterns and activity preferences) that is built incrementally with the use of the system. This model is fed into the rule-base system that provides recommendations on the amount and type of activity, hence combining SL with FB, UT and GS. As mentioned previously, the system seems to be partly in conceptual phase and no technical details are provided. The AAFS intends to be a continuously adapting system that takes into consideration the user’s routine, for GS, and their progress through time regarding the psychological constructs, for Ad. The system uses a smart reference module that automatically plots in a graph self-adjusting goal lines for each individual based on their routine of activity. The goal lines provide information on the percentage of total activity that the user should achieve at different times of the day and are built based on the past user activity, on that day of the week and specific time of the day, but with a slight increment. Furthermore, users are prompted with self-efficacy and stage of change questionnaires that, combined with the classification of their activity pattern, allow the system to automatically select the best feedback strategies for each user. The Sweetch app uses machine learning to create insights about the individual's life habits (e.g.: schedule, activity patterns, driving and walking routes, surroundings) and then, using advanced algorithms adapts the timing and content of FB, CA recommendations and GS. The system learns what types of messages result in better compliance for the specific user in terms of for example, day of the week, time, location and types of messages. However, no details are provided regarding its implementation. C. Behavior Change Theories Only 6 out of 17 analyzed systems explicitly included behavior change theoretical foundations (see Table II). All were classified into the category of “real-time coaching over daily life activities”. The most reported BCT was the Goal-Setting Theory (GST) [67], used in 4 of the systems. Other included theories were the Transtheoretical Model (TTM) [16], [68], Presentation of Self in Everyday Life [69], Cognitive Dissonance Theory (CDT) [70], Social Cognitive Theory (SCT) [71], Self-Efficacy from SCT [72], Self-Regulatory Principles of Behavior Change [73], Social Influence Theory (SIT) [74], Operant Conditioning Principles [75], [76], and Self-Determination Theory (SDT) [77]. 3 systems used a combination of BCTs, all including the GST. The papers did not clearly report design decisions based on these theories. D. Gamification Only 6 out of the 17 systems explicitly mentioned the use of gamification. However, 10 included certain motivation features that could be associated with gamification. Game design elements included avatars (n = 1), challenges (n = 1), leaderboards (n = 3), levels (n = 3), progress (n = 14), rewards (n = 8), social interaction (n = 5), success feedback (n = 3) and theme (n = 4). A total of 42 instances of implemented gamification elements were found across the 17 systems (see Table II). E. System Evaluation The reviewed papers reported on the evaluation of 15 out of the 17 included systems (see Table III) [39]-[41], [44], [46]- [48], [51], [53]-[62], [64], [66], [78], [79]. 6 of these systems were evaluated in more than one separate study, with 4 being submitted to both nonexperimental and intervention studies. In total, 10 systems were submitted to nonexperimental studies (including user experience, system functionality, validation, usability and user acceptance). 9 systems were evaluated in terms of system effectiveness, 5 in randomized experiments, and 4 in quasi-experiments. None of the quasi-experimental studies included a control group. The quasi-experiment and randomized experiment studies, involved between 10 and 55 participants, and 27 and 199 participants, respectively. Regarding duration, the quasi-experiment studies lasted between 1 session and 3 months, and the randomized experiment studies between 10 days and 6 months. From all studies, only 4 compared the effects of different persuasive and/or personalization strategies. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 8 IV. DISCUSSION In this scoping review we identified 28 studies, published between 2007 and 2018, describing 17 real-time PA coaching applications that used some form of personalization. The review contributed with the analysis of 10 new mobile applications and 1 updated version of an already reported system, compared to previous results reported by op den Akker et al. in 2014 [5]. Furthermore, we took a comprehensive approach, following the PRISMA-ScR, gathering detailed information on general system characteristics, personalization, behavior change foundation, gamification and system evaluation. The global picture of having 17 personalized real-time PA coaching systems being published in the last 10-11 years reveals a considerable interest of the scientific community in this field of research. However, we expected more contributions taking advantage of the current complexity and accuracy of smartphones and monitoring technologies in implementing more advanced real-time personalization. Prior work [5] has provided a clear conceptual framework for tailoring/personalization, and provided numerous ways to explore different tailoring mechanisms for the design of new and more complex systems. Yet, none of the reviewed papers referred to such work or any other personalization or tailoring theories. Hence, many gaps remain to be addressed in this field. TABLE II SUMMARY OF PERSONALIZATION, THEORETICAL FOUNDATION AND GAMIFICATION. a Systems also covered in the literature survey by op den Akker et al. in 2014 [5]. FB - Feedback; GS - Goal Setting; UT - User Targeting; IHI - Inter-human Interaction; Ad - Adaptation; CA - Context Awareness; and SL – Self Learning. TTM - Transtheoretical Model; GST - Goal Setting Theory; SCT - Social Cognitive Theory; SDT - Self-Determination Theory; SIT - Social Influence Theory; CDT - Cognitive Dissonance Theory. The presence of gamification elements was classified with (✓) – for implicit use - or ✓ - for explicit use. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2019.2947243, IEEE Journal of Biomedical and Health Informatics JBHI-00215-2019 9 TABLE III SUMMARY OF SYSTEM EVALUATION METHODS