The use of persuasive systems and devices to reduce sedentary lifestyles and encourage individuals to be more physically active is progressively gaining interest. This paper presents a 13 years review (from 2006 to 2019) of personalized persuasive technologies (PTs) for promoting physical activity (PA) and discouraging sedentary behavior (SB). Moreover, we deconstructed the various implementations and operationalizations of the personalization in these PTs and compared their effectiveness. Specifically, this paper aims to (1) shed light on the multiple ways of implementing personalization in these PTs to promote PA and reducing SB, (2) evaluate the effectiveness of personalized PTs for promoting PA and decreasing SB, (3) highlight trends and offer suggestions for research in the area of personalizing PTs. Persuasive 2020, Adjunct proceedings of the 15th Int. conference on Persuasive Technology.
Persuasive technologies (PTs) are interactive computing systems, apps, or devices that are purposely developed to influence users to adopt healthy behaviors and attitudes and prevent risky ones without using coercion or deception [15], [41]. PTs are mainly implemented to promote healthy behavior and prevent disease or to manage diseases or other health conditions [38], [40], but also have been used in other domains. Sedentary behavior (SB) is generally characterized as a long time sitting behavior – when an individual expends lower or equal to 1.5 metabolic equivalent (£ 1.5 MET) [57], [44]. MET “is defined as the amount of oxygen consumed while sitting at rest and is equal to 3.5 ml O2 per kg body weight x min” [25]. Accordingly, SB and an insufficient amount of PA are global health concerns, as they lead to obesity and morbidity, and they are the fourth leading reason for mortality globally, with an estimated 3.2 million deaths worldwide [60]. Therefore, increasing or maintaining a suitable level of moderate-intensity PA is essential for avoiding or mitigating different diseases and health complications such as obesity, cancer, diabetes, and cardiovascular diseases [12], [19].
Persuasive Technology (PT) interventions are considered powerful tools for helping people to adopt healthy behaviors such as maintaining or increasing PA levels and reducing sedentary lifestyles. PTs are often deployed using various technological platforms (e.g. fitness activity trackers and sensors, smartphones, websites, social networking sites (SNSs), games, desktop computers, and ambient displays) [1], [41]. Activity trackers, for instance, are often used to track and monitor personal data (e.g. the user’s PA level, step count, heart rate, and time spent sedentary), and these activity tracker devices are used mostly with other technology platforms (e.g. smartphones, web-based apps) to provide users feedback about their PA progress, personalized feedback, and personalized recommendations [1], [32], [26].
Research has suggested that personalizing PTs increases their effectiveness [39]. Thus, personalizing PT intervention is crucial because people are different with respect to their personal preferences, activity levels, objectives, requirements, lifestyles, and health conditions, and even personality [42], [43], [46]. As a result, there is an increasing demand for PTs to be personalized to increase their effectiveness. The personalization strategy requires a system to provide personalized content or services to increase relevance, motivation, and persuasion effects [22].
There is an increasing number of reviews of PTs for health and wellness. However, most of these reviews focused on the general area of health and wellness and their general application of various persuasive strategies, for example, see Orji and Moffat [41], and Aldenaini et al. [1]. Research has highlighted the importance of personalizing PT, and the personalization strategy is one of the most frequently employed persuasive strategies [3] used in PT designs. Hence, there is a need for in-depth research into analyzing various implementations of personalization in persuasive strategies and the effectiveness of personalized PTs.
Therefore, this paper presents a 13-years review
An increasing number of systematic reviews is being conducted to determine the effectiveness of PTs in various domains. Ghanvatkar et al. [19] provided a scoping review of personalization strategies employed in PA interventions. They included 49 eligible studies in their review paper. They examined personalization strategies in the form of feedback or recommendations. Furthermore, they identified six types of a personalization strategy based on different forms of implementation in their reviewed studies.
There are other interesting review papers on PT interventions in the area of PA and/or SB in general – not focus specifically on personalization. For example, Almutari and Orji [2] provided a systematic review of articles that focused on PT for promoting PA. They analyzed the effectiveness of PT that employed social influence strategies such as comparison, cooperation, and competition only. Their findings revealed that PTs employing social support strategies to promote PA are promising in motivating users to be physically active.
Similarly, Aldenaini et al. [1] conducted a 16-years systematic review
Furthermore, Wang et al. [58] conducted a systematic review of studies targeted at reducing SB in the work environment. They used the persuasive system design (PSD) model [22] to evaluate the effectiveness and utilization of PT in discouraging prolonged SB among office works. Their findings showed that a reminder was the most frequently employed PSD strategy. They also found that coupling a reminder strategy with education sessions was more promising than hourly reminders alone.
Our systematic review paper included studies that employed a personalization strategy in their PT design to promote PA and/or reduce SB. In contrast, many existing studies tend to focus only on either promoting PA or reducing SB, and hardly on both. We also aimed at examining various ways of implementing personalization in different PTs. Again, our review specifically focused on PTs employing a personalization strategy in the area of SB and PA.
duration of exercise, calorie burn rate.
3
As this paper aims to evaluate and analyze PT interventions (e.g. systems, apps, or websites) that implemented a personalization strategy, we used quantitative content analysis to classify data into different categories and compare between different outcomes [48].
We used Google Scholar, ACM Digital Library, Springer, PubMed, Elsevier Scopus, and EBSCOHost databases to search for and select relevant studies. We searched the terms “Persuasive Technology and Physical Activity”, “Persuasive Technology and Sedentary Behavior”, “Technology and Physical Activity Interventions”, “Technology and Sedentary Behavior Interventions”, “Fitness and Persuasive Technology”, “Prolonged Sitting and Sedentary Lifestyles”, “Physical Activity Apps or Applications”, and “Sedentary Behavior Apps or Applications”. We also searched through the relevant references from the reviewed studies. We used Mendeley, a reference manager, to organize the obtained articles.
After searching through different databases, we identified 534 unique titles, of which 315 articles were excluded by title, while 219 articles were considered eligible based on title examinations. We investigated each title to check whether it falls within the scope of the review or not. We excluded those titles that targeted other health domains – domains not related to PA and/or SB (e.g. dental health, smoking cessation, unwanted pregnancy, eating habits, risky sexual behavior, alcohol drinking, etc.). After examining the abstract of the 219 remaining articles, we excluded 181 articles, and we included a total of 38 articles. The included articles: were published in English between 2006 and 2019; implemented any form of a personalization strategy, included personalized recommendations or feedback to promote PA and/or reducing SB; and were targeted at PA or SB, or both. We summarized the search and exclusion process, as shown in Figure 1.
We coded the 38 relevant articles by adapting the coding scheme created by Orji and Moffat [41] and validated by many other researchers, including [1], [3], [2]. As shown in Table 2, the coding sheet contains the study author(s), year of publication, domains, technology platforms, duration of a study, evaluation methodology, persuasive strategies, theories, targeted outcomes, audience age demographic, number of participants, results, and country of a study. We added a new identification to the coding sheet, which is the type of personalization as we adapted from Ghanvatkar et al. [19]. The types of personalization refer to the different ways of implementing a personalization strategy throughout the PT interventions. Appendix 1 provides a comprehensive overview of our coding sheet.
Country of a study where conducted.
4
Our analysis of existing PT interventions that applied personalization in their system designs for PA promotion and/or SB reduction revealed interesting outcomes. This section presents our findings from the reviewed studies in detail. 4.1
Persuasive Technology for Physical Activity and Sedentary Behavior by
Based on our inclusion and exclusion criteria, Figure 2 shows that most of the articles were published between 2016 and 2019. Three articles (8%) were published in each of 2010, 2012, 2013, and 2014. One article (3%) was published in each of 2006, 2007, 2008, and 2011. Fig. 2. Persuasive Technologies for Physical Activity Promotion and Sedentary Behavior Reduction Trends by Year.
All the studies included in this review were targeted at either promoting PA and/or discouraging SB. We categorized the targeted health domains into three groups: PA, SB, and mixed (the studies that focused on both PA promotion and SB reduction). Thirty-two studies (84%) were targeted at maintaining or increasing PA, while four studies (11%) focused on decreasing SB. Only two studies (5%) targeted both the PA and SB domains. Figure 4 presents the targeted health domains covered in this review paper.
Evaluation Methodologies Used for Promoting Physical Activity and
The number of participants in the evaluation of the PT interventions for promoting PA and discouraging SB varies significantly across the reviewed studies. The number of participants ranged from 4 to 129,010. For the reviewed studies that had multiple phases, we combined the number of participants from all phases.
As represented in Figure 7, sixteen studies (42%) were targeted at adults (31–54 years old), while six studies (16%) were targeted at young adults (18–30 years old). Children and elderly people were targeted in five studies (13%) for each. Four studies (11%) targeted teenagers, and only six studies (16%) did not specify their target population. 4.6 Effectiveness of Personalized PTs Across Various Age Groups
All six studies that targeted at young adults reported fully successful outcomes. For children, out of the five studies targeted at them, three studies (60%) showed fully successful outcomes, one study (20%) was partially successful, and one study (20%) did not evaluate its PT design. Out of the five studies targeted at elderly people, four studies (80%) were fully successful, and only one study (20%) did not provide an evaluation for its design. Three studies (75%) targeted at teenagers reported fully successful outcomes, and only one study (25%) targeted at teenagers was partially successful. Out of the six studies that did not specify their target population, two studies (33%) were fully successful, and four studies (67%) had no evaluation.
Most reviewed PTs implemented a personalization strategy as personalized feedback and personalized recommendations. With respect to feedback, the personalization strategy was mostly employed as personalized educational or personalized motivational content. However, for the recommendations, personalization was employed as personalized activity recommendations, personalized goal recommendations, or personalized fitness partner recommendations in line with Ghanvatkar et al. [19].
According to Ghanvatkar et al. [19], there are six types of a personalization strategy, as shown in Table 1. Thus, we adapted their categorizations of personalization and used it in analyzing the personalization approaches employed in the reviewed studies. The comprehensive details about each type/category of personalization and their definitions can be found in Ghanvatkar et al. [19]. Besides the above personalization approaches, other implementations of a personalization strategy were identified in the reviewed studies. For instance, Dantzing et al.[10] implemented personalization in the form of context-aware coaching, as users got a daily personalized step goal, and they were coached through a custom-design smartphone application to achieve their targeted goal by receiving personalized messages in realtime. Thus, the work of Dantzing et al.[10] provided an example of three types of personalization: goal recommendations, educational content, and motivational content. Another example implementation of the personalization as goal recommendations and motivational content was found in Bounce [31], a smartphone app for breast cancer survivors that encourages them to engage in more PA. The Bounce app reminds users of their PA goals. It also provides personalized and customizable reinforcements and virtual rewards such as badges and trophies once users achieve progress towards accomplishing their goals and sends pop-up messages to congratulate users on their PA progress.
Schafer et al. [50] delivered personalization as personalized motivational content. They provided personalized gamified feedback through a smartphone application. This feedback was delivered by providing a simple visualization of the activity level through a personalized animated avatar and represented them based on the gender of children. Francillette et al. [17] showed an example of implementing personalization as personalized goal recommendations and intervention timing. They designed a smartphone exergame app to motivate individuals with severe mental health conditions to integrate PA into their daily lives. The app enables players to plan and set their PA goals based on the players’ profiles, which allows the system to generate different PA choices with different difficulty levels according to the players’ predetermined choices as personalized goal recommendations. These PA choices were delivered to the players at suitable times as personalized intervention timing.
The PRO-Fit application [11] showed an example of implementing personalization as personalized fitness partner recommendations, goal recommendations, activity recommendations, and intervention timing. PRO-Fit is a personalized smartphone fitness application that recommends personalized PA sessions based on the user’s calendar and availability, fitness goals, and preferences. Furthermore, the app integrates with the user’s social networks, and, based on them, the app suggests “fitness buddies/partners” that have similar fitness goals, preferences, and availability.
Table 3 provides a summary of different ways of implementing a personalization strategy in our reviewed studies based on the types of personalization that have been classified, defined, and validated by Ghanvatkar et al. [19] in their scoping review study.
mind users about their target quantified goals (e.g. personalized context-aware coaching, generating different
preferences). For example of implementations, see [31], [10], [11], and [17], etc.
Personalized Fitness Partner Recommendations Personalized Motivational Content Personalized Educational Content Personalized Intervention Timing
have the same target goals to increase their motivations (e.g. suggesting “fitness buddies/partners” that have similar fitness goals, preferences, and availability). For example, see [11].
Sending personalized feedback or messages that aim to motivate users to change their behavior of engaging in more PA (e.g. context-aware coaching that encourages users to continue maintaining a good levels of practicing PA, personalized gamified and visual feedback of the user’s activity level through a personalized animated avatar). For example, see [10], [31], and [50], etc.
awareness and knowledge of the users by providing them with different tips and instructions (e.g. contextaware coaching that educate users in how to specific type of PA). For example, see [10], and [14], etc.
Intervention timing is a type of personalization that uses the context of the feedback or recommendation and finds the right and suitable time to deliver it to the user (e.g. recommending PA choices PA to be delivered to the players at their suitable times and availability). For instance, see [11], and [17], etc. 4.8 Effectiveness of Personalization in Physical Activity and Sedentary
Figure 11 shows the effectiveness of various implementations of a personalization strategy. Out of the 23 studies that employed motivational feedback, 16 studies (69%) reported fully successful outcomes, five studies (22%) reported partially successful outcomes, and only two studies (9%) had no evaluation of their PT designs. Out of the 19 studies that implemented a personalization strategy as goal recommendations, eight studies (42%) were fully successful, four studies (21%) were partially successful, six studies (32%) did not evaluate their system, and only one study (5%) did not specify its outcomes. Out of the ten studies that applied activity recommendations, six studies (60%) showed fully successful outcomes, two studies (20%) reported partially successful outcomes, and two studies (20%) had no evaluation.
Of the ten studies that employed the personalized intervention timing as an implementation of persuasive strategy, five studies (50%) reported fully successful outcomes, three studies (30%) were partially successful, and just two studies (20%) were not evaluated. Finally, out of the five studies that implemented educational feedback as a form of personalization, three studies (60%) were fully successful, one study (20%) was partially successful, and one study (20%) had no evaluation. Only one study employed a fitness partner recommendation as a form of personalization, and this study did not evaluate its PT design. Fig. 5. Effectiveness of Personalized PT Based on How a Personalization Strategy was Implemented. 5
Discussion We found that many of the reviewed studies (45%) employed a mixed-methods evaluation, which is a combination of qualitative and quantitative evaluation methodologies. We highly recommend researchers to apply the mixed evaluation methodology to fully reveal the comprehensive impact of their PT design and its effectiveness rather than employing either the qualitative method alone or a quantitative method alone. The majority of the reviewed studies targeted adults and young adults with a total of 22 studies (58%). Thus, we recommend that researchers design more PT for promoting PA and discouraging SB targeting other populations such as the elderly, teenagers, and children. Our findings showed that the studies targeted at adults and young adults reported the largest number of successful outcomes, whether fully or partially successful. This is perhaps because adults and young adults are at an active age, and they can practice moderate-intensity PA easily more than older populations. Similarly, adults and young adults are more aware of the benefits of PA and the consequences of a sedentary lifestyle than teenagers and children [1], [2].
Based on the reviewed studies, we found that personalizing PT for promoting PA and discourage SB among users is an effective and promising approach of increasing the effectiveness of PTs, with a total of 29 (76%) of the entire reviewed studies (38) reporting successful outcomes, both fully and partially successful outcomes. We included studies that implemented any form of personalization in the PT design for PA and SB domains. The results of our analysis revealed that personalization was delivered and implemented differently from one study to another based on the user’s profile, preferences, predetermined goals, and availability. We found that the most common and effective way of implementing a personalization strategy based on the reviewed studies emerged to be personalized motivational content. We believe this is because motivation is one of the most crucial factors for persuading users to achieve a particular objective [37], such as, in our case, increasing PA levels and reducing SB. Thus, personalized feedback that aims to motivate users to change their behavior of engaging in more PA and reducing SB plays an essential role in improving users’ motivation and commitment.
The second most frequently employed and effective type of personalization implementation is the goal recommendations. Personalized goal recommendations were delivered to users based on the PA goals they set on the PT system. It is important to mention that goal recommendations describe quantified goals such as step counts, calories burned, and duration of activity [19]. Users appreciated systems that reminded them of their personal goals and objectives.
Based on our findings, personalized activity recommendations and intervention timing ranked as the third most commonly employed and effective implementation of personalization in PTs for promoting PA and decreasing SB. Activity recommendation entails prescribing different types of activities for achieving a set goal such as running, walking, or cycling. Intervention timing focuses on sending feedback or recommendations to users at a suitable and available time for them because users may ignore or forget feedback sent when they are busy with other primary tasks that take greater priority [19].
Although educational content and fitness partner recommendations seem to be the least commonly employed types of personalization implementation, they are considered effective tools to increase the awareness of the users about the benefits of PA for health and general well-being and the consequences of SB and to provide external motivations through matching users with partners who have similar goals and objectives to assist them in maintaining their PA level [19]. Therefore, we recommend researchers to employ a personalization strategy and deliver it to users using different types of implementation based on the users’ needs and combine it with other complementary persuasive strategies to achieve a desired behavior change objective.
Since most of the reviewed studies employ more than one persuasive strategy, this makes it challenging to know which of the employed persuasive strategies contributed to the effectiveness of a PT and lead to the observed behavior change, such as increasing PA levels and reducing SB. Therefore, we cannot attribute the observed results on the effectiveness of the PTs to their use of a personalization strategy only.
Conclusion This paper presented some interesting trends on various implementations of a personalization strategy and provided a general overview of personalized PT interventions for promoting PA and discouraging SB. We uncovered various ways of implementing personalization in different PTs and compared their effectiveness, including personalized motivational and educational content feedback, personalized goal and activity recommendations, personalized intervention timing, and personalized fitness partner recommendations. Finally, our findings revealed that PT interventions for promoting PA and decreasing SB are effective and promising when they are personalized. 11. 12. 13. 25. 26. 39. 40. for encouraging healthy eating behavior. International Journal of Human Computer Studies. https://doi.org/10.1016/j.ijhcs.2013.08.012 Rita Orji, Regan L. Mandryk, and Julita Vassileva. 2017. Improving the efficacy of games for change using personalization models. ACM Transactions on Computer
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He and Agu (2014), [23] Fahim et al.(2017) , [14] Mohadis and Ali (2016), [34]
SB SB PA
Smartphon e mobile, Computer, Pedometer none none none motivational content and goal recommendation activity recommendations, and intervention timing educational content, activity recommendation, intervention timing goal recommendations, and activity recommendation
APPENDIX 1 Appendix 1. A Comprehensive Overview of PT for Physical Activity and/or Sedentary Behavior # Authors Do- Technol- Applica- Persuasive Strategies Theo- Evaluaof Arti- main ogy tion / Pro- /Affordances ries tion cles, ject Name Method Year, Reference 1 Foster et PA StepMatron none Mixed al.(2010), [16]
Tracking, Personalization, Goal Setting, Self-Monitoring, Social Support (Social Learning, Comparisons, Competition, Rankings Recognition, Giving Comments) Tracking, Reduction, Tunneling, Tailoring, Personalization, Goal Setting, Self-Monitoring (Self-Reflection), Reminder, Suggestion, Liking Tracking, Personalization, Self-Monitoring, Reminder Tracking, Reduction, Tunneling, Tailoring, Personalization, Self-Monitoring, Simulation, Rehearsal, Praise, Reminders, Suggestions, Similarity, Expertise, Real world feel, Third-Party Endorsement, Verifiability, Social Support (Social Learning, Social Comparison, Normative influence, Social facilitation, Competition, Recognition) 8 0 8 Persuasive 2020, Adjunct proceedings of the 15th Int. conference on Persuasive Technology. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 3 Weeks
UK Appendix 1. (continued) # Au- Dothors of main Articles, Year, Reference 5 Cambo SB et al.(2017 ), [7] 6 Mansart PA et and al.(2015 SB ), [30] 7 Lin et PA al.(2011 ), [29] 8 9 Tracking, Reduction, Tailoring, Personalization, Goal Setting, Self-Monitoring, Simulation, Reminder, Suggestion, Liking, Social Role, Surface Credibility, Social Support (Social Comparison) none none none MBR, TTM, DCM, SCT, SRT, HAPA none none none none
Tracking, Reduction, Personalization, Self-Monitoring, Reward, Social Support (Competition) Tracking, Reduction, Personalization, Self-monitoring, Simulation, Rewards Personalization, Self-Monitoring, Rewards, Reminder, Social Support (Competition, Recognition) Tracking, Personalization, Goal Setting, Self-Monitoring, Praise, Rewards, Reminder
TPB, TMB,
PRT Application / Project Name
Persuasive Strategies /Affordances none 5 Minutes
Attitude SDT 1 Week none 5 15 26 30 Appendix 1. (continued) # Au- Dothors of main Articles, Year, Reference 14 Skriloff PA et al.(2016 ), [51] 15 16 17
McMah on et al.(2013 ), [33] Ramanathan et al.(2012 ), [47]
PA PA and Experience Sampling PA and Obesity
Ready~Stea dy ohmage PiNiZoRo
Reduction, Personalization, Goal Setting, SelfMonitoring, Simulation, Praise, Rewards, Social Role Tracking, Personalization, Self-Monitoring, Feedback from users (Self-Report), Praise, Surface Credibility, Social Support Tracking, Reduction, Personalization, Simulation
Persuasive Strategies /Affordances
Tracking, Personalization, Social Support (Cooperation, Competition) none none none none none 0 4 WMT, USS, TDP none goal recommendations none none none none none none
Tracking, Personalization, Goal Setting, SelfMonitoring, Praise, Reminder, Social Support (Comparison, Competition, Sharing)
Tracking, Reduction, Personalization, Self-Monitoring, Praise, Social Support (Cooperation, Competition, Sharing) Tracking, Personalization, Goal Setting, Self-Monitoring, Reward or Punishment, Reminder, Social Support (Cooperation, Competition) Tracking, Personalization, Goal Setting, Self-Monitoring, Social
Support (Sharing) none
Mixed
Persuasive Strategies /Affordances
Tailoring, Personalization, Goal Setting, Self-Monitoring, Praise, Reward, Reminder, Suggestion, Social Support (Sharing)
TTM
Mixed
Toscos et al.(2008 ), [56] Toscos et al.(2006 ), [55] Sohn and Lee (2007), [52] 5 Types of Personalization (Ways of implementation) goal recommendations, motivational content none
Mixed
6 Days none
5 USA none 2 Months Marcu et al. (2018), [31] Appendix 1. (continued) # Au- Domain thors of Articles, Year, Reference 24 Zhang PA and Jemmot t (2019), [59] 25 26
et Lee al. (2018), [28] Lane et al.(2014 ), [27]
PA SB
and PA, Sleep, Social Interaction
Reduction, Tunneling, Personalization, Goal Setting, Self-Monitoring, Praise, Rewards, Reminders, Social Role, Trustworthiness, Expertise, Authority, Social Support (Normative Influence, Cooperation, Social Interaction)
TTM, SCT motivational content , goal recommendations
USA Behavior, Attitude, Awareness, Motivation Tunneling, Personalization, Goal Setting, SelfMonitoring, Praise, Rewards, Reminder, Liking Tracking, Personalization, Self-Monitoring, Simulation, Rewards, Liking, Social Support (Social Interaction) none goal recommendations
none none
Quantitative
19 Days motivational content 34 27 none Hong et al.( 2013), [24] Appendix 1. (continued) # Au- Domain thors of Articles, Year, Reference 28 Francil- PA lette et al.(2018 ), [17] 29 30
Dantzig et al.(2018 ), [10] Altmeyer et al.(2018 ), [4]
PA
Tracking, Personalization, Self-monitoring, Praise, Reminder, Suggestion Reduction, Tailoring, Personalization, Goal Setting, Self-Monitoring, Rewards, Reminder, Liking Tracking, Personalization, Self-Monitoring, Rewards, Reminder, Similarity, Social Support (Comparison, Normative Influence) 12
Partially Successful none Tracking, Reduction, Tunneling, Tailoring, Personalization, Goal Setting, Self-Monitoring, Suggestion, Praise , Trustworthiness, Expertise, Surface Credibility, Real-world Feel, Authority, Thirdparty Endorsements, Verifiability, Social Support ( Normative Influence, Social Interaction) 7 USA Persuasive Strategies /Affordances none none Types of Personalization (Ways of implementations) goal recommendations, intervention timing Schafer et al.(2018 ), [50] Ciravegna et al.(2019 ), [9]
PA
PA Appendix 1. (continued) # Au- Dothors of main Articles, Year, Reference 33 Oyibo PA et al.(2019 ), [45] 34
PA Samariya et al.(2019 ), [49]
Tracking, Personalization, Self-Monitoring, Praise, Rewards, Liking Tracking, Reduction, Personalization, Selfmonitoring, Goal-Setting, Praise, Rewards, Reminders, Expertise, Real-world feel Tailoring, Personalization, Goal-Setting, Self-Monitoring, Rewards, Social Support (Social Learning, Social Comparison, Cooperation) Tracking, Personalization, Goal-Setting, Self-Monitoring, Social Support (Social Learning, Social Comparison) none Behavior, Awareness, Motivation, Acceptance,
Attitude none none goal recommendations none
Motivation, Awareness none none
USA
Tracking, Reduction, Per- none sonalization, Social Role goal recom- none mendations none none
9
Portugal none none educational content , motivational content none none none
UK
Oliveira et al.(2016 ), [36] Economou et al.(2017 ), [13]
PA and Eating (Diet)
Gamified Mobile Web App
PhytoCloud
Tracking, Tailoring, Per- none sonalization, Goal-Setting, Self-Monitoring, Suggestions, Trustworthiness, Expertise, Surface Credibility, Authority, Third-Party Endorsement, Social Support (Social Learning, Normative Influence, Recognition (Ranking), Sharing ) Appendix 1. (continued) # Au- Domain thors of Articles, Year, Reference 37 Geurts PA et al.(2019 ), [18] Tracking, Tunneling, Tailoring, Personalization, GoalSetting, SelfMonitoring, Praise, Expertise, Social Support (Sharing)
Fully Successful
Belgium
A gamified smartphon e app
Woody
Tracking, Per- none sonalization, Simulation, Reminder, Rewards, Liking, Expertise ABBREVIATIONS MBR: Model-Based Reasoning TTM: Transtheoretical Model DCM: Dynamic Computational Model SCT: Social Cognitive Theory SRT: Self-Regulation Theory HAPA: Health Action Process Approach TPB: Theory of Planned Behavior TMB: Theory of Meaning Behavior PRT: Personality Theory SDT: Self-Determination Theory WMT: Wellness Motivation Theory USS: User-Specific Strategies TDP: Theoretical Design Principles GST: Goal-Setting
Behavior, Awareness, Motivation
Austria Persuasive 2020, Adjunct proceedings of the 15th Int. conference on Persuasive Technology. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).