1 Introduction

A common target of e-health interventions is behavior change towards an increased health-related behavior. This might refer to e.g. less alcohol and nicotine consumption, increased physical activity, less stressful lifestyle or work-life-balance, safer sexual behavior, medication adherence, or a more positive treatment motivation in general. The latter includes the affinity towards the uptake of indicated, evidence-based health care measures (Baumeister et al. 2008; Renneberg and Hammelstein 2006; Schwarzer 2004). Two research areas have been recently combined to investigate possibilities to increase the likelihood of behavior change: (1) The field of health psychology-research provides a longstanding expertise on theories and interventions that relates to motivation and more generally health behavior change (Oinas-Kukkonen and Harjumaa 2009; Riley et al. 2011); and (2) the field of (media) informatics, which has developed and examined a multitude of technological features that can foster motivating strategies. These two research fields combined, introduced as persuasive design, might enable scholars to address a common and fundamental challenge in the field of evidence-based health care by dealing with the lack of sufficiently motivated patients who cannot be motivated in sufficient numbers in on-site face-to-face settings (Van Ballegooijen et al. 2014; Wangberg et al. 2008). Persuasive design thus allows for more sophisticated and welcomed Internet- and mobile-based health interventions (IMI) by overcoming two of the major challenges in this context: a general low uptake rate and high attrition rates (Ludden et al. 2015; Riley et al. 2011). Along the described challenges, the chapter at hand provides a summary of (1) treatment motivation and behavior change theories, (2) technological approaches to support behavior change and (3) the integration of both fields to leverage the potential of e-health behavior change interventions.

2 Treatment Motivation and Behavior Change

Motivation (latin: motus = motion) refers to a theoretical construct that defines the direction and intensity of a behavior. Motivation is a key predictor (a) towards behavior and (b) maintenance of this behavior, mediated by one’s own volition to realize intentions. Thereby, volition refers to the means one chooses to realize the intended behavior and the efforts as well as commitments one is willing to invest (Ryan et al. 2011).

Over the last decades, a multitude of health behavior change theories have been established aiming to facilitate the understanding about the reasons why people do (not) live and behave in a beneficial way to their health, especially when considering the present health risks associated with a risky lifestyle or a dysfunctional behavior. Therefore, in the following, the current state of theories on health behavior change is summarized based on Baumeister et al. (2008), providing the basis for developing and implementing technological and digital persuasive solution for facilitating intended behavior.

2.1 Health Behavior Change Models

Only a few decades ago, health behavior change knowledge of professionals was rather simple and straightforward. For example, a physician suggests something and the patient adheres to it, such as “smoking can kill you”, with the expectation that this risk indication will actually stop people from smoking. It became quickly obvious that such approaches, associated with a paternalistic communication style that ignores patients´ attitudes and motivations, do not work (Schwarzer 2004). Thus, while risk perception, as described as a core predictor of behavior in the health belief model (Becker 1947), is still a valid and integral part of most models, two further core predictors of one’s intention have been established in several other models such as the theory of planned behavior (TPB; Ajzen 1985), the social-cognitive theory (SCT; Bandura 2001), the transtheoretical model (TTM; Prochaska and Velicer 1997), the health action process approach (HAPA; Schwarzer 2008), or the technology-related Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al. 2003): Predictor (1) self-efficacy and Predictor (2) outcome expectancy. Note that these constructs, in addition with risk perception are the three predictors of intention as defined in the HAPA model (Schwarzer 2008). Furthermore, they can be found in a similar way, but often denoted differently in many other models (see Table 17.1).

Table 17.1 Overlapping constructs of health behavior change models (modified after Renneberg and Hammelstein 2006; Baumeister et al. 2008)
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2.1.1 Risk Perception

Risk perception as a predictor of motivation (most often operationalized as behavior intention) combines the perceived severity of risks (e.g., diseases following alcohol consumption) and the perceived vulnerability of a person (Baumeister et al. 2008). Thereby, risk perception is viewed as a necessary, but not sufficient condition for behavior change. While one might not think of changing anything in case of a lack of risk perception, it is known that when solely communicating the risks associated with a specific behavior such as smoking, alcohol consumption, or risky sexual behavior does not change the respective behavior at large (Ferrer and Klein 2015; Schwarzer 2004). Therefore, the transtheoretical model (Prochaska and Velicer 1997) additionally specifies that for a risk perception leading to intention and actual behavior change, a person must consider the value of a risk negatively for one’s own life. Cognitions such as “what do I care if my life is short but lived to the fullest” exemplify the gap between general risk perception and health behavior actions. In other words, humans are always motivated, but maybe not towards the directions health care professionals and caring third parties expect them to be. Thus, next to negatively valued risk perception, one particularly needs to believe that the intended behavior change can be achieved (self-efficacy) and it results in a favorable outcome (= positive outcome expectancy) (Baumeister et al. 2008; Hardcastle et al. 2015; Schwarzer 2004; Sheeran et al. 2016).

2.1.2 Self-efficacy

Self-efficacy refers to the subjective certainty of being capable to master new or challenging situations due to one’s own competency (Schwarzer 2004). The construct was introduced in the social-cognitive-theory (SCT) (Bandura 2001) and is viewed as a core predictor of health behavior change (Hardcastle et al. 2015; Schwarzer 2004; Sheeran et al. 2016). Thereby, a distinction between generic and context specific self-efficacy has been suggested (Schwarzer 2004). While generic self-efficacy describes a global assessment of one’s own confidence of being capable to solve new or challenging tasks, context-specific self-efficacy refers to the expectation of being able to handle a specific situation (e.g., quit smoking, start or maintain physical activity). Current health behavior change models further differentiate self-efficacy by regarding the phases of the health behavior change process, starting with motivation-related self-efficacy (confidence of being able to achieve the goal), followed by volition-related self-efficacy (see below) (Baumeister et al. 2008).

2.1.3 Outcome Expectancy

Different to the construct of self-efficacy, which is used similarly across the different theories, outcome expectancy occurs in most models, but is labeled differently with also varying connotations (Schwarzer 2004; Renneberg and Hammelstein 2006). At least, implicitly the models define outcome expectancy as a subjective cost-benefit assessment by regarding the expected outcomes of behavior changes. In some of these models, such as the SCT (Bandura 2001), outcome expectancy already includes the construct social norm, while others, such as the TPB (Ajzen 1985), and the UTAUT (Venkatesh et al. 2003), define outcome expectancy as a separate predictor. Thereby, social norm has been theorized as working through both a social pressure to act (e.g. spouse kindly suggesting to lose weight) and an anticipated reinforcement by meaningful others (e.g., anticipated compliment given to the improved body shape) (Schwarzer 2004). While the latter refers to outcome expectancy (anticipated approval), the first rather can be explained as operant conditioning (negative reinforcement due to the expected discontinuation of the social pressure once the behavior has been changed).

A similar controversy exists regarding the perceived costs of an action. For example, if one thinks of reducing alcohol consumption, a person’s expected negative consequence might be abstinence symptoms (= outcome expectancy). Probably, this person would anticipate at the same time that lowering alcohol consumption would be accompanied by substantial emotional stress (= perceived costs of the action). These aspects do not exactly match with the term outcome expectancy and might better be operationalized as action-related expectancies. In the process of an action, such expectancies might refer to cognitions prior (e.g. expected opportunity costs like “when I go jogging twice a week on top of everything else I can´t watch my favored TV series anymore), during (“I will be quite exhausted and fun is something else”), and after the action (“I will be in such a good shape”).

2.1.4 Intention

Intention is the construct that describes the case that a person decides to change a respective behavior, given a present risk perception and associated psychological strain, sufficient task specific self-efficacy, and outcome expectancy. For a long time, intention has been the postulated core predictor of health behavior change (Knoll et al. 2005). However, as we all experienced failures in regard to New Year’s resolutions, the difference between intention and actual behavior change becomes obvious. This is introduced as the intention-behavior-gap phenomenon (Conner 2008; Sheeran and Webb 2016; Sutton 2008) and has led current health behavior change models, such as the HAPA (Schwarzer 2004), to introduce a volitional phase, following on one’s intention to change a behavior.

2.1.5 Volitional Factors of Health Behavior Change

The term volition refers to a process that focuses on the actual realization of a behavioral intention. In the social sciences, volition is seen as a construct that is linked to the philosophical discussion on a free will, which limits the possibility of empirically examining the process behind the intention-behavior-gap, with a still ongoing controversial discussion whether volition can be validly assessed (Zhu 2004). However, in the fields of health psychology and motivation research, volition has become an inherent part of modern health behavior change theories (Heckhausen 2007; Renneberg and Hammelstein 2006; Schwarzer 2004).

The HAPA model for example describes a three stepped volitional phase, consisting of a pre-actional, actional and post-actional phase (Schwarzer 2004; Zhang et al. 2019). In the pre-actional phase, intentions are transformed into more specific plans about when, where, and how the intended behavior shall take place (“action planning”, Sniehotta et al. 2005; “implementation intentions”, Gollwitzer (1999). Additionally, one should anticipate possible barriers and challenges to successfully conduct the intended behavior, such as situational temptations (e.g., going in a pub with friends while trying to stay abstinent; “coping planning”, Sniehotta et al. 2005). The actional phase is characterized by conducting the intended behavior and maintaining it over time (e.g. gym visits twice a week for the next year). Research shows that even freely chosen health behavior actions conducted as part of an experiment already lasted 66 days (median) to become an automatism (Lally et al. 2010). Hence, a core challenge in this actional phase is to protect the intended behavior against alternatives, sometimes tempting motives and aims until the behavior has become part of one’s daily life. Finally, the action is evaluated in the post-actional phase and becomes reinforced according to the concept of operant conditioning, which is theorized to impact the reoccurrence of a behavior in dependence of the evaluation and reinforcement.

Again, self-efficacy has been postulated to be a core factor in this volitional phase, with a sub-categorization into “action self-efficacy”, referring to one’s believe of being able to conduct the behavior, “coping self-efficacy”, referring to ones believe of being able to protect the planned behavior against other plans and temptations, and “recovery self-efficacy”, referring to the believe in one’s own ability to recover from setbacks instead of showing disengagement (Schwarzer 2008).

2.1.6 Person- and Personality Characteristics Associated with Health Behavior Change

Inter-individual differences regarding the health behavior baseline as well as differences in the ability of health behavior change are well documented (Kaprio et al. 2002). Most prominently, gender has been examined extensively. For a long period, women were seen as less prone to drinking, smoking, and unhealthy diet, but less physically active compared to men; a view that might have become more complex (McDade-Montez et al. 2007). Regarding age, most risk behaviors decrease with increasing age, while physical activity becomes less likely (McDade-Montez et al. 2007). Most importantly in this context is that gender or age are not causal for health behavior, but different bio-psycho-social factors make a specific behavior more likely in one population compared to another. This becomes obvious when looking at the simplification of homosexuality being the core risk factor for HIV in the 1980s. Not homosexuality, but unprotected sexual behavior was always the causal risk factor, which has more frequently been practiced by homosexual men (Hammelstein et al. 2006). Focusing on homosexuality, instead of unprotected sexual intercourse in risk communication and prevention strategies, might therefore be the reason for heterosexual intercourse having become more frequently been associated with HIV than homosexual intercourse in the following years (Hammelstein 2006). Next to socio-demographic variables, personality traits such as the “big five” openness, conscientiousness, extraversion, agreeableness and neuroticism have been suggested as relevant moderators of health behavior and behavior change (Bogg and Roberts 2004; McDade-Montez et al. 2007; Roberts et al. 2005). Thereby, conscientiousness and agreeableness have been associated with positive and neuroticism with negative health behavior (McDade-Montez et al. 2007), while results are less conclusive regarding openness and extraversion. Further personality constructs are frequently discussed, such as optimism as way of interpreting information, which might impact ones outcome expectancy (Hammelstein et al. 2006; McDade-Montez et al. 2007; Schwarzer 2004) and self-directedness as the ability to regulate and adapt behavior to individually chosen, voluntary goals (Cloninger et al. 1994; Sariyska et al. 2014). Finally, mental disturbances, such as depressive symptoms, are discussed as motivational and volitional barriers towards health behavior change, which one might misinterpret as being non-compliant (Baumeister et al. 2008).

3 Persuasive Design: Technological Features to Enhance Health Behavior Change

Digitalization is currently a frequent keyword when it comes to preparing our healthcare services for future challenges, particularly for an aging population with tremendous health care needs in resource-limited healthcare systems (Singh et al. 2016). Several technological solutions for a variety of health conditions, mental disturbances and unfavorable lifestyles have been developed in the last years (Christensen et al. 2009; Day and Sanders 2018; Van Ballegooijen et al. 2014; Wangberg et al. 2008). However, intervention adherence is often one of the core limitations of these otherwise helpful interventions (Baumel et al. 2017; Baumel and Yom-Tov 2018). Persuasive design is one of the constructs that specifically focuses on this human-machine-interaction problem, for which the machine would do the trick if only the human would work like a machine (Kok et al. 2004; Muench and Baumel 2017; Perski et al. 2017). Conflicting motives, a lack of self-efficacy, perceived high costs of the behavior, unfavorable outcomes expectancies, a lack of self-efficacy, as well as missing skills and potential temptations in the volitional phase are the key factors, for which persuasive interventions can make a difference (Venkatesh et al. 2003).

In the last decade, there has been substantial research efforts in the area of persuasive design. Persuasive technologies are defined as interactive systems, which are intentionally designed to influence their users in order to change their attitude and/or behavior (Fogg 1998). These technologies and their design principles can further be categorized in (a) primary task support, (b) computer-human dialogue support, (c) system credibility and (d) social support (Hamari et al. 2014; Oinas-Kukkonen and Harjumaa 2009).

Primary task design principles aim to support the user by achieving his primary goal when using the system (e.g., a successful intervention). The design principles in this category include reduction of complex behavior, guiding the user through the system, tailoring and personalization of content, as well as providing functionalities for self-monitoring (e.g., by visualizing and tracking progress), simulations, or (virtual) rehearsals of behavior. Tikka and colleagues (2018), for example, presented a gamification approach to promote rehearsals by repeatedly letting the user play a food categorization game in order to improve their game score. However, their study results indicate that rehearsal may not be enough to result in a positive behavior change (Tikka et al. 2018). Anagnostopoulou and colleagues (2018), as a second example, used personalized persuasive messages in a route planning application in order to motivate users to opt for more environmentally friendly route choices. These context-aware messages implemented self-monitoring (e.g., by providing feedback for past traffic usage) and suggestion (e.g. by suggesting to walk short distances) as persuasive features, and were perceived as useful by the users within a pilot study (Anagnostopoulou et al. 2018). Self-monitoring and mood/behavior-feedback systems have also been implemented frequently in health apps (e.g. Kauer et al. 2012; Montag et al. 2019). However, the component effects of such monitoring features in particular, or primary task design principles in general, are still largely unknown.

Computer-human dialogue design principles are supposed to help the users to move towards their goal or target behavior by implementing system feedback (e.g., audio, visual or textual), through direct feedback (i.e., positive and negative reinforcement), rewards (i.e., gamification through credits, points and achievements), reminders and alerts, suggestions and advice, as well as by designing the system in a way that it is appealing to its users and adopts a social role for them (e.g., by incorporating virtual agents). Reddy and colleagues (2018) conducted a feasibility study in this field of persuasive design technology for a phone-based recommendation system with the goal to change energy usage behavior at home, showing that recommendations may influence participant behavior by increasing their contextual awareness. Another field study showed that an animated character can be used as an imaginative trigger to foster healthy smartphone use (Chow 2018). As a third example, Wais-Zechmann and colleagues (2018) used personalized reminders and rewards to assist in meeting physical activity goals for patients with COPD (chronic obstructive pulmonary disease). They investigated the perceived persuasiveness within an online study utilizing storyboards, and concluded that these persuasive strategies are rated above average (Wais-Zechmann et al. 2018). While such automatic prompts and reminders are already part of established and well evaluated Internet- and mobile-based interventions (Bendig et al. 2018; Domhardt et al. 2018; Ebert et al. 2018, 2017), the question on when to prompt and remind users in what way and dosage to achieve the best possible behavior change is an open question not yet well understood (Baumeister et al. 2014; Domhardt et al. 2019; Fry and Neff 2009).

Design principles in the system credibility category focus on designing a system that is credible to its users by providing verifiably qualified, truthful, fair and unbiased information, demonstrating experience and competence, having a competent look and feel, and referring to real-world people and respected third-party endorsements. Wais-Zechmann and colleagues (2018) state that information and suggestions coming from an authority (like physicians or acknowledged institutions) are more persuasive for persons with COPD. Several interventions have already been developed and examined that used such persuasive messages referring to authorities (e.g. doctors and experts) in order to improve participants’ intervention expectancy and adherence (e.g. Lin et al. 2017a, b; Sander et al. 2017; Spelt et al. 2018). Whether such authority focused approaches are indeed the best way to optimize system credibility, however, is a question for future studies, which should compare the persuasiveness and effectiveness of authority based strategies against other possible approaches such as professional look-and-feel strategies, strategies including a buddy avatar, or strategies using labels and certificates of well-respected organizations.

Finally, design features in the social support category describe how to design the system in a way that motivates its users by leveraging social influence through functionalities to observe, compare, and learn from other users as well as facilitating interaction, cooperation, competition, and recognition of successfully achieving behavior change goals, e.g., through the sharing of leaderboards or rankings (Hamari et al. 2014; Naslund et al. 2017; Oinas-Kukkonen and Harjumaa 2009; Orji and Moffatt 2018). Examples for this type of features are: interactive tools like messaging and chats with other users, user groups, social media sharing functions, rankings, the possibility to follow and mentoring functions (Mylonopoulou et al. 2018). Wunsch and colleagues (2015) implemented persuasive strategies in order to encourage biking as low-energy mode of transportation by utilizing recognition (awards based on the number of bike rides), competition (email updates with a leaderboard), cooperation (collective goals), and social comparison (options to compare the number of bike rides with others). They observed an increase in bike sharing for participants receiving the intervention as compared to the control group (Wunsch et al. 2015).

Overall, the field of persuasive design is still in its infancy and the correlation between health-related behavior change and persuasive design enhanced interventions is still unclear. A recent systematic review concludes that in 75% of the included studies persuasive design was superior to standard design in regard to health behavior change, whereas in 17% of the examined studies positive and negative outcomes have been reported. Finally, 8% of the studies reported no effect of persuasive design on the intended health behavior change (Orji and Moffatt 2018). Hamari and colleagues (2014) reported in their literature review 52% of positive, 36% of mixed, and 7% of negative outcomes related to persuasive design approaches in health behavior change.

Altogether, when it comes to sustained behavior change in the real world, the different persuasive design components in the system (e.g., feedback, rewards, support) should correspond properly to create a holistic user experience that helps change human behavior in real life. For example, providing people with feedback and rewards without adapting the program based on a user’s progress, or without making sure they understand the expectations and relevance of the intervention before they begin, might fail the creation of the holistic experience that is expected to nurture a behavior change. Another aspect that is key in nurturing such an experience is that the quality of persuasive design is important and not only the question of whether a certain checklist of different components was included within the development process. For example, rewarding a person by offering a badge, if it cannot be presented to a group of people this person cares about and who also understand the meaning of the badge, would probably not achieve that same outcome as the intrinsic reward for doing an activity for oneself that can be mirrored through a compassionate statement.

Trying to answer these gaps, recent research introduced the concept of therapeutic persuasiveness, which is the way a program is designed as a whole to encourage users to make positive behavior change in their life. Therapeutic persuasiveness includes (1) call to action (e.g., goal setting, prompts), (2) load reduction of activities, (3) real data-driven/adaptive content (monitoring of user state and ongoing adaption of the intervention according to a user’s individual progress), (4) ongoing feedback and rewards, and (5) clarity of therapeutic pathway and rational (Baumel et al. 2017). In this sense, therapeutic persuasiveness captures the quality of support a user receive from a technological system in his or her own path to achieve the desired goals. Furthermore, therapeutic persuasiveness aims to assess the degree a software is assisting in overcoming emerging difficulties during the behavior change process.

4 A Field Moving Forward

Recent technological progress and research trends as well as the prevalence of mobile devices and wearables create promising opportunities in the field of persuasive design. In Table 17.2, the most present persuasive design techniques in the scientific literature are heuristically mapped to the underlying psychological factors that predict health behavior change. While this mapping is based on expert consensus only and should therefore be interpreted as preliminary, it illustrates the broad range of technological approaches for each psychological behavior change dimension. However, common persuasive design techniques address mostly outcome expectancy and self-expectancy, while particularly the important predictors of the volitional phase goal setting and planning are less frequently taken into account yet. Therefore, persuasive design approaches that aim to increase health behavior change should include strategies for this volitional phase as well in order to not only facilitating intention, but leading to an actual behavior change.

Table 17.2 Persuasive design techniques and proposeda corresponding predictors of health behaviour change models
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The acceptance and broad use of mobile devices (such as smartphones, smart watches, etc.) will further provide opportunities to improve the persuasiveness of forthcoming health behavior change approaches. Particularly, opportunities to collect vast amounts of data, combined with new analytical methods such as deep/machine learning, will enable developers to improve the persuasiveness of their systems. These data can be used to (1) gain deeper knowledge about mental states in real-life situations, (2) get insight into the development, maintenance and course of health conditions, (3) evaluate therapeutic processes, (4) give timely or context triggered just-in-time interventions or to suggest tailored interventions (Brunette et al. 2016; Rathner et al. 2018a, b). This timely feedback, in combination with the experienced social support, reinforce the adoption and maintenance of healthy behaviors (Naslund et al. 2017). Furthermore, the use of wearable sensors can assist in monitoring health and disease management over extended periods of time, as they need little active user input and therefore compliance (e.g. Ben-Zeev et al. 2015; Lanata et al. 2015; Naslund et al. 2017). To make such use of big data sets, deep machine learning will be one promising computational basis (Bengio et al. 2013; Längkvist et al. 2014; Miotto et al. 2018). It is based on an iterative process of computerized pattern recognition and is therefore well suited to analyze big data exploratively. Theories based on the detected patterns can be subsequently tested in confirmatory study designs and therefore may lead to deeper knowledge. Overall, the use of persuasive design to foster health behavior change will improve the likelihood of success in future.