7 Motivation, Engagement, and Flow

I can hear the threatening moans of the undead gaining on me from behind. Picking up the pace, I break into a run, and my heart quickens. I round a sharp corner, cut through the park, and finally welcome the reassuring news through my earbuds: “Zombies evaded.” I pick up some virtual medical supplies and head for home.

Jogging for your life in the midst of a zombie apocalypse is just one of the many ingenious ways designers have conceived to get people motivated in the modern world.1 The reality is, I’m not the Nike+ type (Dorian, here). I don’t feel like an amazing athlete, not even with a wristband or strategically triggered applause. But immerse me with agency in the unfolding of a satirical suspense narrative, and I’ve managed a heart-pumping run through the neighborhood – all before breakfast.

Motivation and wellbeing intermingle in sophisticated ways. Not only is motivation fundamental to taking any kind of positive action, but the absence of it is a hallmark of depression. Clearly, a life rich in motivation is more rewarding than life without.

Motivation is a trigger to act, and when that activity is sustained by an ongoing urge to carry on, we are engaged. We might be engaged in writing an article, playing Frisbee, or laying bricks to build a house for someone homeless. Our level of engagement may be barely enough to keep us going, or it might be all-encompassing, sweeping us into a state of flow (that place of optimal engagement famously described by Mihaly Csikszentmihalyi.)

It’s hard to imagine a technology designer who wouldn’t be aiming to motivate users in some way, be it to download, upload, collaborate, contribute, click through, or learn more. As such, there are many resources with advice on how to do so more effectively through design. This chapter distinguishes itself by taking the less considered angle of motivation as a contributor to psychological wellbeing. We look at the interrelated notions of motivation, engagement, and flow. We consider key motivation theories, giving special attention to those that provide an explicit bridge between motivation and wellbeing. We then turn to current technologies to seek out hints of how motivation theory might be applied to the design of future things to increase users’ psychological wellbeing.

Motivation

The Pleasure Principle

At the most primitive level, motivation can ultimately be viewed as the desire to seek pleasure and avoid pain. With origins stretching as far back as ancient Greece at least, our understanding of motivation has at various points been contested, reinforced, and expanded by psychologists, economists, and philosophers who have integrated concepts as diverse as altruism, primal urges, autonomy, cognitive dissonance, and interconnectedness into the mix.

Motivational theories, too numerous to cover here, have generally focused on either social underpinnings, psychological drivers (e.g., cognitive), and biological factors. We look at several of the major theories and issues and begin with a point of contention impossible to omit from any discussion of motivation.

Intrinsic and Extrinsic Motivation -- a Sibling Rivalry

If I engage in an activity because it’s fun, I am said to be intrinsically motivated. In a sense, the activity is its own reward. If I engage because I fear the stick or crave the carrot, I am said to be extrinsically motivated. The carrot represents a reward separate to the task (e.g., money, points, or approval), and the stick is, of course, a punishment (e.g., exclusion, demotion, or imprisonment), each of which resides outside myself and is controlled by some mechanism external to me (a parent, a boss, or a judge). As Richard Ryan and Edward Deci (2000) put it, “The most basic distinction is between intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something because it leads to a separable outcome.”

Our environment is replete with examples of extrinsic motivators, and most adults now living were educated by a system that relied on it almost exclusively. Nowadays, the mere mention of a stick is a bit cringeworthy and suggests the controversies that have arisen over what motivators are effective, desirable, or just. Although many psychologists and educators have all but abandoned the stick, researchers such as David Greene, Mark Lepper, and Edward Deci have warned that the carrot can be similarly destructive.

In an often cited seminal study (Greene & Lepper, 1974), preschoolers offered a reward to do something intrinsically motivating (draw pictures) lost their intrinsic motivation and drew less than children asked to do it without a reward. This is just one in a slew of similar studies that have exposed the potential for extrinsic motivators to undermine intrinsic motivation, and growth in this area of research has led to a number of modern revisions of how we should structure our workplaces and societies.

Offering contingent rewards (“if you do this, then you get this”) can turn something enjoyable into work, a shift that, over time, degrades intrinsic motivation and may condition us always to need rewards to be motivated. Because intrinsic motivation is associated with quality learning, felt competence, persistence, creativity, positive coping, and wellbeing, then sabotaging it is counterproductive and, some would suggest, a contributor to society-wide problems.

But, obviously, we can’t always be intrinsically motivated to do all that needs to be done in a day, and this is where extrinsic motivators become important. Indeed, Ryan and Deci (2000) argue that certain kinds of extrinsic motivation share many of the benefits of its intrinsic sibling and that the important difference lies in autonomy. They provide a model that may prove invaluable to work in positive computing because it separates extrinsic motivation into four categories, each with a “perceived locus of causality” that is more or less externally derived:

• External regulation, which is entirely “external” (e.g., compliance).
• Interjection, which is “somewhat external” (e.g., seeking approval).
• Identification, which is “somewhat internal” (e.g., activity is valued).
• Integration, which, like intrinsic motivation, is “internal.” In this case, external regulators are assimilated to the self.

For example, a student memorizing a list of pharmaceuticals because she’s desperate to graduate med school and become a doctor is acting in a way that is self-determined, even though the memorizing itself is not intrinsically fun. She has identified the task with a life goal and the causality can therefore be considered “somewhat external.” Someone who volunteers his time to stuff envelopes for Amnesty, isn’t doing it because stuffing envelopes is a blast, but because he is motivated by a compassionate desire to help others and seek justice. The motivation is extrinsic to the task, but highly self-determined and therefore beneficial and rewarding. The task aligns with his core values, and he may have fully integrated this type of activity with himself.

Ryan and Deci’s (2000) review of a number of studies reveals that extrinsic motivation that is more internal (and therefore more autonomous or self-determined) is associated with greater engagement, better performance, higher-quality learning, and greater psychological wellbeing (many of the same benefits attributed to intrinsic motivation).

Social influence such as reciprocity, gratitude, positive self-image, and career goals are all commonly observed motivators for people who engage with social media, all of which can be mapped to Ryan and Deci’s taxonomy of human motivation. In thinking about design for wellbeing, it is useful to know that the range of motivators that are more internally derived will better support wellbeing. Thus, aiming to support more internally derived forms of motivation rather than relying too heavily on simple contingent rewards (or the simplistic imposition of shallow game mechanics) is a worthwhile pursuit in the context of positive computing.

The most obvious deployment of every type of intrinsic and extrinsic motivation in technology design today is mastered in games and seen in the application of game mechanics to nongames, also known as “gamification.” According to information technology and research advisory company Gartner (2011), by the time you read this, “more than 50 percent of organizations … will [have] gamif[ied] [innovation] processes.” Gamification rewards can be used in many ways, including those that undermine intrinsic motivation, but also in effective ways that add motivational layers of enjoyment to inherently unpleasant tasks or as feedback to reflect growing competence. We look at some examples later in this chapter.

Motivation That Is Intrinsic to Being Human

In the case of the volunteer and the medical student mentioned earlier, it may be helpful to look at their goals as stemming from innate human needs, such as purpose, connectedness, competence, and self-actualization (depending on the theory you employ). Although much motivation is contingent on individual interests, some drivers are considered universal to being human. Most obviously there are the physiological drives that urge us to satisfy hunger, protect ourselves from the elements, and procreate. Moreover, many games are built on the natural human motivation to seek patterns in visual information, collect things, connect with others, and resolve cognitive dissonance (sometimes manifest as mysteries, puzzles, or conflicting information). Some of the key theories describing such innate human motivators are described in the next subsection.

Drives, Needs, and Desires

At the foundation of modern motivational theory sits Abraham Maslow’s (1943) hierarchy of human needs. According to this influential theory, we are driven by five levels of needs: “It is quite true that man lives by bread alone –when there is no bread. But what happens to man’s desires when there is plenty of bread and when his belly is chronically filled?” Maslow goes on to answer this question with a list of need categories, each prerequisite to the next, suggesting that a new category is “unlocked” only once the previous category has been reasonably satisfied.

1. Physiological needs, such as food, air, and sleep are primary. Only when these needs are met (and, Maslow argues, they generally are outside of emergency situations) can humans move on to other needs.
2. Safety refers to our need for security and stability as well as to safety from physical danger. Insurance plans, career decisions, savings accounts, burglar alarms, and deep freezers can be looked on as ways in which we are motivated by our need to feel relatively protected from harm and loss. Our desire to accrue, collect, and build things, be they real supplies or achievements and virtual collectables in a game, might be linked to this underlying need for stability and safety nets.
3. Love follows once the first two need categories are fairly well satisfied, and it includes belongingness and the giving and receiving of affection. Various types of digital environments allow us to develop a sense of belongingness to a group, connect with people whom we love or may come to love, and share in affection. (Sending an intimate text message, “poking” someone, or using the kisses emoticon in a chat box can be interpreted as virtual signs of affection (or “affiliative design,” as mentioned in chapter 6).
4. Esteem or the high evaluation of oneself by oneself and by others is a recognizable need. Maslow points specifically to esteem-related desires for strength, achievement, adequacy, confidence, reputation, independence, and freedom. (We discuss self-awareness, self-esteem, and self-compassion in detail in chapter 8.) Much of the way we measure our sense of worth in the modern world is mediated by technology. Whether it’s in endorsements, profiles, “likes,” or eXperience points, technology has an undeniable impact on our capacity to feel and extend esteem.
5. Self-actualization refers to a person’s tendency to reach his potential, to achieve the most he can become, and to feel fulfilled. As Maslow describes it, “A musician must make music, an artist must paint, a poet must write, if he is to be ultimately happy. What a man can be, he must be. … This tendency might be phrased as the desire to become more and more what one is, to become everything that one is capable of becoming.” New models of open education such as open content and massive open online courses provide opportunities for people to explore creative potentials or pursue mastery. Ideally, positive computing will come to increasingly support people in building human psychological potentials such as mindful awareness, compassionate action, and emotional intelligence.

In the search for fundamental human needs, others have followed Maslow. Steven Reiss (2004) has proposed a set of 16 basic desires, most of which can be filed into Maslow’s hierarchy, with the exception, perhaps, of idealism, power, and vengeance. More recent theories on human motivation have given concepts related to autonomy and competence a more central role, such as Ryan and Deci’s SDT mentioned in chapter 2. Also among the most notable is the work of Carol Dweck.

"I Think I Am" versus "I Think I Can" -- Fixed and Growth Mindsets

In her early work, Stanford University psychologist Carol Dweck (Dweck & Leggett, 1988; Dweck, 2006) identified two types of behavioral responses: a “helpless” pattern in which people tend to avoid challenges, view obstacles negatively, and reduce performance; and a “mastery-oriented” pattern observed among people who seek challenges and who are persistent in the face of obstacles. In her book Mindset (2006), she describes these patterns of behavior as being related to “fixed” and “growth” mindsets respectively.

Simply stated, those with fixed mindsets believe their abilities arise from innate capabilities and intelligence endowed at birth that cannot be changed. In contrast, those with a growth mindset believe their abilities are developed over time and can be enhanced, a view that is in far greater alignment with recent discoveries on neuroplasticity and epigenetics. It turns out that these two subtle variations in how we view ourselves lead to striking differences in behavior and wellbeing.

According to Dweck’s research, those harboring a growth mindset are significantly better at identifying their strengths and weaknesses, and when faced with a setback, they tend to look for learning opportunities. In contrast, those with a fixed mindset are more focused on judgments. When a fixed-mindset person is confronted with a setback, her tendency is to judge herself, as in “I am a failure.” Even when outcomes are positive, there is a tendency to compare herself to others: “I am better than the others.” Research has shown that the fixed mindset is more likely to lead to unhappiness.

Cognitive-behavioral therapy helps people detect helpless patterns and is one of the most successful modern strategies for helping people with depression. The work of Dweck and others such as Heidi Grant, a motivational psychologist at Columbia University’s Motivation Science Center, have been influential to practices in education, management, and personal development. Some applications of this work include advice to parents and caregivers to praise children for effort and improvement rather than innate intelligence (“Good job, you’ve really improved” or “You worked hard for that” rather than “Good job, you’re so smart” or “I bet you are the smartest in your class”).

These findings can also inform the design of applications that help people set and follow up with goals. Goal-setting tools span a broad spectrum, including those that draw explicitly on psychological theory as well as those that focus more on technical sophistication. What Dweck’s research shows us with regard to wellbeing is that it’s not just the goals you set that are important, but how you think about them and about yourself in relation to them. Therefore, goal-setting tools can impact wellbeing not only by supporting wellness-related goals (I vow to run more or eat better), but also in terms of how they support thinking around goal setting. Trash talk, leaderboards, task breakdown, and deadlines are all approaches to motivating someone toward reaching a goal, and these strategies may or may not have positive impacts on wellbeing depending on the context. Goal setting has been shown to be highly effective in many contexts, but it is not entirely free from caveats when it comes to wellbeing.

Goal Setting -- Implications for Wellbeing

Goal-setting theory has provided a framework for investigation into how and under what circumstances defining goals influences things such as performance, self-efficacy, and satisfaction, particularly within organizational and educational settings. Explicit goals are used as motivators, and researchers such as Edwin Locke and Gary Latham have consistently found that setting specific, difficult goals leads to higher performance by individuals and teams. As such, goals are often used, especially in workplaces, to boost productivity and achievement. However, Lock and Latham’s research also shows that the effectiveness of goal setting is dependent on contextual factors such as task difficulty, competence, framing, and self-efficacy. They discuss some of these moderators of goal effects in their essay “New Directions in Goal-Setting Theory” (Locke & Latham, 2006).

Others have examined the effects of goal setting, goal attainment, and disappointment in the area of personal development and mental health. At a broad level, research has found that “individuals valuing relationship goals above achievement oriented goals have been found to have a greater sense of wellbeing than individuals placing achievement goals above relationship goals” (Street, 2002). Helen Street also reviews how goals can either worsen or improve depression and how variables such as goal definition, content, type, and framing play a part. She also explores those situations in which people relentlessly pursue goals regardless of personal cost.

Some have questioned what they consider to be the overuse of goal setting, particularly in American culture. D. C. Kayes (2004) used the 1996 Mount Everest disaster (in which six expert climbers climbed to their deaths despite all safeguards and information urging them to return) as an example of when relentless adherence to an original goal can become irrational and cause team breakdown. “In the face of an environment that requires learning, short-term project teams may encounter the limits of the positive effects of goal-setting.”

More research is needed before we can fully understand the effects of goal setting on practices such as mindfulness and positivity. Mindfulness is frequently described as a method for letting go of plans for the future and nonjudgmentally settling into the realization that “just being is enough.” Goal setting seems decidedly antithetical in this context. Yet the current standard for most wellness technology is to apply tracking features that can encourage both goal setting and comparison to others (“I will practice mindfulness every day this month” or “I failed to be grateful 10 times this week” or even “I can’t believe my brother beat me on meditation today”). For certain types of positive-computing technologies, it may be the relinquishing of goal setting that requires support, at least for some user groups and particularly because many of us currently live in otherwise heavily goal-driven societies.

Clearly, technology designed to promote motivation and wellbeing through goal setting must keep in mind the need for balance and be skeptical of overly simplistic views of goal-setting psychology. As always, a technology team’s greatest safeguard is collaboration with mental health professionals.

There is still much investigation to be done around the effects of various types of tracking, goal setting, self-evaluation, and game mechanics with regard to their roles in appropriate balance – for example, balance between positive thinking and heartfelt authenticity, between directedness and present-moment attention, between drive and calm, desire and contentment, dissatisfaction and acceptance, and other balance relationships related to goals that impact psychological wealth and wellbeing.

Social Motivators

Another motivator intrinsic to being human lies in our tendency to be influenced by the actions and opinions of others. Social psychologist Erving Goffman (1959) proposed that much of human behavior is motivated by how we would like others to see us, and his theory provides a way of conceiving the public versus private lives we keep online. Goffman’s interactionist theory was based on his “dramaturgical approach,” in which behavior is seen as a series of minidramas in which individuals perform in front of an audience, and both are participants in a performance. The performance has a front stage on which the audience sees the individual and a backstage representing where the individual is when alone. Some have described Goffman’s work as “social phenomenology” (Miles & Huberman, 1994).

Sunny Consolvo and Katherine Everitt (2006) propose a set of guidelines based on Goffman’s and Leon Festinger’s (1957) theories for designing systems that encourage physical activity: (1) give proper credit; (2) provide history, current status, and performance measures; (3) support social influence (i.e., use social pressures and support); and (4) consider practical constraints. These guidelines have since contributed to successful systems (Consolvo, 2009a) as well as to more generic and theory-driven guidelines for behavior change (Consolvo, 2009b).

Consolvo (2009b) argues that technologies that support behavior change should support impression management, the individual’s movement between Goffman’s “front-stage” and “back-stage” behaviors. She urges that these technologies need to allow users to manage backstage access. For example, if a user wants to misrepresent an event or conceal an action, the system should support this type of behavior. (Not everyone wants his or her personal foibles or failed exercise routines made public.) This requirement offers social affordances that are common in day-to-day life (we don’t advertise these in person either).

The combination of Goffman’s work and cognitive dissonance theory (Festinger, 1957) provides a useful framework for the design of behavior-change applications, but they do rely on some important assumptions – for example, that our self-control goals always increase our chances of goal achievement. Yet we all have experience with some level of rebelliousness. One of us (Dorian) believes that if a phone were to tell her to put down that brownie because the digital scale sent data to say that she was getting fat, she would probably break the phone and eat two brownies. Work on “ironic processes” (Wegner, Schneider, Carter, & White, 1987) has demonstrated that when trying hard not to think about something, people will think about it more.

Moreover, as mentioned in the chapter on positive emotions, denying negative feelings or enforcing positive thinking via affirmations can have damaging effects to wellbeing. Moreover, there will be cases in which some people’s personal health goals are self-destructive (as in the case of those with anorexia, for example). As part of future research in personal informatics, behavior change, and positive computing, we will need to work on better understanding such complicating issues and their relationship to design and to find ways to devise designs that favor balanced and holistic approaches.

With regard to social influence as a motivator, many current-day apps and websites supply features that allow users to share their milestones and other personal data with others as a way of leveraging the motivational effects of social support and pressure. Of course, the impact of social pressure is not simply always good or always bad. Clearly, the effects of, say, trying to quit smoking may be different depending on whether you are doing it privately or publicly with your friends watching. Peer pressure is notorious in its connotations regarding teen behavior, such as drug use and risk taking. More generally, research by Sonja Lyubomirsky and others (Lyubomirsky & Ross, 1997; White, Langer, Yariv, & Welch, 2006) have highlighted the negative correlation between social comparison and happiness. Furthermore, studies by social psychologists have shown how negative influences can spread in a population (Christakis & Fowler, 2007).

At the same time, a multitude of successful mental health and wellbeing programs, including Alcoholics Anonymous, coming-out programs, and the SuperBetter resilience app, encourage the participant to connect with a sponsor or ally. Positive role models, mentors, and social proof can be just as productively influential as the negative variety can be detrimental. An ongoing research and practice question for positive-computing researchers will be: How can we design to promote positive social influence, while preventing the social validation and spread of destructive patterns?

The Delicate Issue of Persuasion

The psychological research on motivation described earlier makes it clear that motivation is a complex and multidimensional construct. Yet as technologists we often seek simplified models to facilitate practical design work. For example, B. J. Fogg’s (2009) behavior model aims for simplicity in the name of practical application and suggests that humans are motivated by pain/pleasure, hope/fear, and social acceptance/social rejection and that designers can boost motivation by manipulating these. This model underpins work in persuasive technology, an area that Fogg describes as “the automation of behavior change.”

The Fogg model is used for diagnosing interaction design problems, uncovering marketing opportunities, and encouraging small daily habits. Of course these are not the same as supporting psychological wellbeing, so we would be unwise to try and transplant Fogg’s model blindly to the positive computing context. For example, highlighting motivators such as social acceptance and fear in ways that exploit low self-esteem or that contribute to constant anxiety (e.g., “Get rid of unsightly belly fat!,” “We are under attack!”), although effective for many of the applications of persuasive technology, can have serious consequences for wellbeing. Furthermore, in positive computing we need to consider different types of motivation (e.g., extrinsic versus intrinsic) because research shows that motivation as it relates to wellbeing is not one size fits all.

Researchers are working to apply ethical guidelines to persuasive technology (as mentioned in chapter 4) and from the perspective of positive computing, we can view the issue as related to impact on wellbeing. There are various types of motivation and different ways of appealing to them, some of which improve wellbeing and some of which don’t. In general, if wellbeing is our aim, as technologists we must be wary of basing our efforts on a simplified view of human beings and thus risking harm. This risk is one of the reasons we argue so strongly for multidimensional evaluation and for partnerships with wellbeing psychologists who will have a broader knowledge of human behavior and of the strengths and limitations of various models.

How exactly we define a range of motivational impact that includes helpful support at one end and manipulation and propaganda at the other will be a point of ongoing professional debate, but surely both transparency of motives and individual autonomy must be central to making the distinction.

As described in chapter 4, there are other approaches to supporting motivation for behavior change. One that has rapidly gained popularity among those working on population-wide wellbeing initiatives goes by the name of the book that popularized it: Nudge.

Nudging Positive Change -- Designers as Choice Architects

Nudge theory can be applied to technology design, but it has been more famously positioned as a model for public policy with the distinct aim to improve organizations and society.

In the book that triggered the wave of interest to follow, Richard Thaler and Cass Sunstein (2008) describe their notion of “libertarian paternalism” that underscores the notion of nudging. A nudge, as they use the term, is “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives.” In essence, it’s about designing things such as policies and information in a way that favors healthier decisions, but without removing choice.

It is perhaps a shame that Thaler and Sunstein opted to use the term paternalism (which they openly acknowledge is saddled with negative and oversimplified connotations). For many, this term obscures the fact that Thaler and Sunstein insist on preserving choice and on basing assessments of improved life satisfaction on the individual’s values rather than on the choice architect’s values.

Perhaps the most compelling aspect of their argument is the notion that it is impossible to be neutral. When we design information technology (as is true for determining policy), we must base our decisions on something, and even if that something is no more than a coin toss, our decisions will have impacts on wellbeing. If that is the case, it seems crazy to base design decisions on mere chance when we might inform them with knowledge of what will improve people’s lives. VSD might look at user values and determine ways of designing to improve user goals based on those values. Positive computing will look at psychological wellbeing and how we can make architectural decisions that are less likely to do psychological harm and more likely to do psychological good, without removing choice. What nudging reminds us is that for any work that seeks to improve lives, it is essential that liberty and autonomy are preserved and that notions of improvement are based on evidence, not on designer opinion (that evidence being research in psychology in the case of positive computing).

Engagement and Flow

In order to move from motivation to ongoing action, one must engage in some sort of activity, and, as such, these two factors are generally studied together. Engagement is quite influencable, so we can purposely design user interfaces and technological interventions that aim to engage people; in other words, our hopes for having impact in this area are well founded.

The Rules of Engagement

To have a prior engagement or be engaged means you have committed seriously to some person or purpose. Likewise, when we are engaged in a digital experience, we have committed to it, perhaps not consciously, but nevertheless it has seduced us to spend our valuable time with it. When educators discuss engagement, they’re speaking about the holy grail of sustained student attention and interest. Positive psychologists often use the term engagement to refer to an active participation with one’s community, with society, or with a higher purpose. Although this term can mean a variety of things, in this chapter we use it to refer to that combination of motivated commitment and sustained attention that educators, psychologists, media designers, and other architects of experience so often seek to elicit from their audiences.

Although there is no unilateral agreement on a precise definition or taxonomy of engagement, there tends to be consensus that when someone is engaged in an activity, they are more likely to enjoy it, to produce quality outcomes, and to learn more (Graham & Weiner, 2012). Most of the academic work on engagement has focused on student engagement in school and employee engagement on the job.

Researchers in the learning sciences (Christenson, Reschly, & Wylie, 2012) provide a definition of engagement that breaks it down into four types:

• Emotional engagement is assessed by detecting supportive emotions (e.g., interest) and the absence of negative, withdrawal emotions (e.g., anxiety or boredom).
• Cognitive engagement is demonstrated when the student uses sophisticated rather than superficial learning strategies.
• Behavioral engagement is generally assessed by observing concentration, attention, and effort (e.g., time on task). This type of engagement is the most straightforward to measure automatically – for example, by using computer vision techniques or behavioral analytics based on the digital traces left during online activities.
• Agentic engagement is signaled by the student’s active contribution to her learning experience (e.g., through creativity and innovation).

High levels of engagement are frequently described as being contingent on appropriate challenge, autonomy, and intrinsic motivation. Parvaneh Sharafi, Leif Hedman, and Henry Montgomery (2006) have mapped these attributes onto a multidimensional model they describe as the engagement modes (EM) model. The EM model specifies five engagement modes: (1) enjoying/acceptance, (2) ambition/curiosity, (3) avoidance/hesitation, (4) frustration/anxiety, and (5) efficiency/productivity. These modes are described on three dimensions: evaluation of object, locus of control between subject and object, and intrinsic or extrinsic focus of motivation. In the EM model, flow emerges when the user faces a pleasurable challenge that is also possible to master.

Flow -- the Holy Grail of Engagement

Flow represents a state of total involvement in autotelic (intrinsically motivated) activity because the activity is so engaging it removes one from reflective self-consciousness. It’s hard to imagine a game designer or software architect who wouldn’t be thrilled to hear that their users were entering states of flow. This reason alone has made it worthy of study among technologists looking to design more rewarding digital experiences.

As part of the Oxford Handbook of Positive Psychology, Jeanne Nakamura and Mihaly Csikszentmihalyi (2009), the originator of flow theory, identify a number of requirements of flow experiences, which include perceived challenges that neither overwhelm nor underutilize our skills, clear reachable goals, and feedback that indicates if we are getting close to these goals. When these requirements are met, it is possible to get into “flow,” an experience that combines:

1. Concentration on the present moment
2. Perceived agency over the situation or activity
3. A merging of action and awareness
4. A loss of reflective self-consciousness
5. A distortion of temporal experience
6. Intrinsic rewards

How many technological features are designed to increase productivity, but in ways that become a hindrance to flow experience? (Think of beeping phone notifications.)

Csikszentmihalyi (1997) describes flow as an “optimal experience” and a key to happiness. Thorough treatment of the varied affective and cognitive consequences of flow ranging from increased subjective wellbeing and greater life satisfaction to addiction on the extreme negative end are well beyond the scope of this book but are included in the collection Advances in Flow Research (Engeser, 2012).

Detecting and Measuring Engagement

Both emotional and cognitive engagement occur internally, and, as such, analysis has relied either on subjective reports or on instruments such as the Motivation and Engagement Scale (Martin, 2007), often used in pre/postexperimental designs, measurement studies, and path modeling. Both of these techniques have the limitations associated with after-the-fact reporting (Liem & Martin, 2012).

Flow has been measured through interviews, surveys, and experience sampling. More recently, advances in emotionally intelligent interfaces (Grafsgaard, Wiggins, Boyer, Wiebe, & Lester, 2013) and computer modeling allow us to approach the measurement of engagement constructs in new ways. In particular, sensing- and affective-computing techniques allow for ways of integrating observed expressions of engagement with internal self-reported measures. These techniques use sensors such as video cameras to record voices, facial expressions, and physiology (Calvo & D’Mello 2010). Moreover, we can generalize data from subjective reports in order to detect engagement automatically from a wider number of users more easily and with increasing accuracy.

Kazuo Yano, Sonja Lyubomirsky, and Joseph Chancellor (2012) describe an experiment in which flow was detected by physiological sensors as consistency of movement: “The key indicator of flow turned out to be consistency in movement. For some people, that consistent movement was slow; for others it was fast. Some were morning people; others favored the afternoon or evenings. Regardless, when participants experienced flow, their motions became more regular, as they lost themselves in a challenging but engrossing activity.”

Engagement and Games

When we’re not using our digital devices to engage with learning or work, there’s a good chance we’re using them to play. We’ve been playing games from before we could write (personally and historically), which suggests that games meet basic human psychological needs. One of the great powers of games is their ability to engage us fully. Gaming is an area in which levels of engagement can be particularly high, so digital game research is an obvious place to turn for guidance on engaging users in aid of wellbeing, but also for insight into when engagement becomes addiction and impacts wellbeing for the worse.

So do videogames increase or decrease wellbeing? According to research, the answer is: both. Our challenge is to pull apart the fibers of the digital gaming experience until we can better understand which characteristics improve wellbeing in what contexts and which don’t.

When it comes to books, whether reading improves or decreases your wellbeing depends largely on content. With videogames, content is significant, but there are also critical wellbeing implications for how a game is played and who it’s played with (Johnson, Jones, Scholes, & Colder Carras, 2013) – in other words, the social context and the game mechanics.

Exposure to violent videogames has consistently been shown to increase aggression, desensitize to violence, and reduce prosocial behavior. For example, a recent meta-analytic review (Anderson et al., 2010) concludes that “the evidence strongly suggests that exposure to violent video games is a causal risk factor for increased aggressive behavior, aggressive cognition, and aggressive affect and for decreased empathy and prosocial behavior.”

But not all games are violent, and, unsurprisingly, just as practiced aggression can increase aggression, prosocial games can increase prosocial behavior (Gentile et al., 2009). It’s not just the content that matters, but how you play. Game mechanisms vary both in general and in the details, and we suspect that key wellbeing differences lie in both core game mechanisms and in those easy-to-miss details.

The negative effects of videogames are fairly well publicized, but research on the positive effects of games on both psychological and physical wellbeing is less well-known. A survey of randomized controlled studies (Baranowski, Buday, Thompson, & Baranowski, 2008) on the use of videogame interventions for improving mental and physical health-related outcomes revealed moderate to strong positive results across 38 studies representing 195 health outcomes. These studies used videogames to provide physical therapy, psychological therapy, and improved disease self-management, among other things. Interestingly, positive effects were strongest (69 percent) for psychological outcomes.

Only seven of the studies were psychological interventions, and they included the use of casual, strategy, and custom games for post-traumatic stress disorder, anxiety, age-related cognitive decline, dyslexia, attention capacity, and self-esteem. Furthermore, many more studies in the literature were not included in this survey because they did not meet the randomized controlled trial criteria. The authors conclude that higher-quality studies remain relatively uncommon in this area and that “in order to most effectively assess the potential benefits of video games for health, it will be important for further research to utilize (1) [randomized controlled trial] methodology when appropriate; (2) longer follow-up duration; (3) improved measures of quality, such as randomization and blinding; and (4) standardized measurement tools and careful attention to the quality of outcome measures.” In other words, early results are promising, but more work remains to be done.

Carmen Russoniello, Kevin O’Brien, and Jennifer Parks (2009) showed the ability of casual games to reduce stress, anxiety, and depression. As mentioned in chapter 6, a recent review of digital games (Johnson et al., 2013) looked at their impact on young people’s wellbeing and concluded that they “positively influence young people’s emotional state, self-esteem, optimism, vitality, resilience, engagement, relationships, sense of competence, self-acceptance and social connections and functioning.”

This impact suggests that games are still largely untapped for positive-computing research and design. Of course, in the midst of our enthusiasm, we must still be careful to resist sweeping generalizations. That some studies have shown that some specific casual games can decrease stress and depression is very significant and means we can design for that outcome – but it doesn’t follow that all games or even all casual games do this for all people or that secondary effects may not also emerge.

By way of anecdotal illustration, many of us have probably already enjoyed the stress-relieving qualities of Bejewelled, the popular game that proved therapeutic in the East Carolina study (Russoniello, O’Brien, & Parks, 2009). However, this same game has multiple incarnations, one of which, Bejewelled Blitz, is styled with all the bells-and-whistle rewards of slot machines and gambling. As enjoyable as I (Dorian) found it myself, I was persuaded to remove it from my tablet when my seven-year-old began begging me for money to use on gambling in the game. I shuddered to think it might be conditioning his formative mind to respond all the more rapidly to this kind of manipulation later in life.

The problem for wellbeing in this scenario is not games in general or even the core game mechanism itself (pattern matching), but the way in which this particular version was designed to profit financially from gambling-based incentives. When I replaced it with an equivalent that employs the same core pattern-matching mechanism, but without the gambling layer, Candy Crush, the negative side-effect disappeared.

My humble experience is hardly research evidence, but it is illustrative of how multilayered and complex the short- and long-term effects of even apparently simple casual games will be on wellbeing (as well as of how design for optimum profit can come up against design for wellbeing). Inevitably, some design aspects of any technology may contribute to wellbeing, while others may decrease it in parallel. This is what seems to occur with cooperative violent videogames that have been correlated with aggression but also cooperation skills (Greitemeyer, Traut-Mattausch, & Osswald, 2012; Velez, Mahood, Ewoldsen, & Moyer-Guse, 2012). Playing as a team, users can learn valuable cooperation skills and find new ways to connect, but doing so in the context of enacting graphic violence or as part of a simplistic “us” versus “them” mentality are separate aspects with potentially negative consequences for the wellbeing of individuals and society. Game designer Raph Koster (2013) suggests that our gameplay (digital and otherwise) continues to reinforce instinctive skills once critical for our survival as cavemen but now obsolete, such as shooting and aiming (once important for hunting). He also cites “blind obedience to leaders and cultism, rigid hierarchies, binary thinking, the use of force to resolve problems, like seeking like and its converse xenophobia” as common themes based on obsolete skills. He suggests that we should be designing games that evolve with us and reinforce skills relevant to the modern world. By way of example, he highlights the game Diplomacy, which according to Koster is not only an example of a game that reinforces modern skills, but one that also provides “evidence that remarkably subtle interactions can be modeled within the confines of a rule set and traditional role-playing can reach the same heights as literature in the right hands.”

Game addiction is another serious concern for anyone looking to use games to foster psychological wellbeing, and research has implicated risk factors such as personality traits, motivations for playing, and structural game characteristics (Kuss & Griffiths, 2011). But what if games themselves could be used to build resilience against game addiction while fostering positive engagement? Work uncovering risk factors (Kuss & Griffiths, 2011) and studies on resilience factors (risk of Internet addiction reduced by self-compassion [Iskender & Akin, 2011]) have begun to pave the way.

Although a discussion on addiction is well beyond this book’s scope, the message emerges that if we are to be genuinely effective in leveraging the incredible potential of games for positive computing, we need to work carefully through what will be a slowly unraveling story of psychological impact and an important ongoing area of research. The key to a future of positive games lies in giving these technologies credit for producing highly multifaceted and complex experiences, acknowledging the incredible potential they provide, and exploring all the effects of game design on wellbeing so that we can increasingly favor the beneficial ones.

Design Implications

Designing to Motivate

The Zombies, Run! game, alluded to at the start of this chapter, is one of a multitude of apps and “exergames” whose primary offering is motivation – namely, motivation to do things we don’t otherwise feel intrinsically motivated to do. We may want to do things that improve our wellbeing, but when those things also require effort or are unpleasant in the moment, we are at odds with the pleasure principle. Technology, sometimes via gamification, can step in to resolve the conflict. By layering experience and challenge that are intrinsically enjoyable (e.g., playing a part in a zombie narrative) over the activity that isn’t (e.g., running), if the two activities are sufficiently intertwined (running becomes part of the story), then the whole experience can become more rewarding, thereby increasing our intrinsic motivation to take part.

Uplifted, created by the United Kingdom’s Channel 4 for promoting positive emotions, takes a slightly different approach and embeds moments of positive reflection into an Angry Birds – style casual game. The game and reflection are thematically linked but not intrinsically linked as they occur separately and have little bearing on one another.

Other approaches engage our self-determined extrinsic motivation by helping us to articulate and track goals, be they larger goals (I will eat healthier) or smaller subgoals (I will chug a glass of water every morning) in aid of closing the gap between our behavior and our goals and values.

Designing for Engagement and Flow

In 2006, Yvonne Rogers (2006) discussed designing for increased engagement as an alternative to quiet automation for a future of ubiquitous computing. Rogers proposes “a significant shift from proactive computing to proactive people; where UbiComp technologies are designed not to do things for people but to engage them more actively in what they currently do. Rather than calm living[,] it promotes engaged living, where technology is designed to enable people to do what they want, need or never even considered before by acting in and upon the environment.” Design for human autonomy (as opposed to machine autonomy) will be critical to fostering wellbeing (Calvo, Peters, Johnson, & Rogers, 2014). An important balancing factor for human autonomy (as pointed out to us by Ben Shneiderman in conversation) is the reality of interdependence and our interconnected relationship to others. Both of these are core to SDT. If we were to look at design for motivation and engagement from the perspective of SDT, then when we evaluate a technology, we might ask these questions: Does the user experience respect autonomy? Does the user experience support a sense of competence? Does the user experience support connection to others? Of course, in being a theory of both wellbeing and motivation, it should also help us create conditions for engagement and flow.

Although it is generally accepted that there is no way to reliably design an experience of flow (the triggers are too individual), there are nonetheless ways to design conditions that increase the likelihood of flow experience. One approach to doing this is to identify the obstacles to flow and design to reduce them. Researchers in attentive computing investigate, among other things, how interruptions can be minimized and attention sustained. Modern versions of popular productivity software such as Microsoft Word and WordPress have incorporated options that remove screen clutter, allowing the user to focus only on the task at hand. Similar examples were Apple’s inclusion of the “Do not disturb” setting in its iOS, and Freedom, a software application whose sole purpose is to allow you to easily shut down your Internet connection for a set period of time so you can proceed without distractions.

Another approach to supporting flow is to identify those conditions that are particularly conducive to it and include them where possible. For example, based on the work by Nakamura and Csikszentmihalyi (2009), we should design for appropriate challenge, clear reachable goals, and feedback that indicates if we are getting close to these goals. Certainly, these conditions are already familiar to game and interaction designers. A third approach to designing for flow, rather than looking at the universals, honors the individuality of what triggers flow for different people. In a study mentioned earlier (Yano et al., 2012), the research team used sensor data to help workers determine what time of day they were most likely to get into flow. They could then adjust their schedules and habits accordingly.

A significant characteristic of flow theory for HCI is its interactionism (a focus on a system made of the person and the environment). Csikszentmihalyi (1997) describes an emergent motivation that arises from the interaction that forms the context of a flow experience rather than a motivation that is a property of the individual (i.e., commitment) or of the environment (e.g., persuasion). Such a line of argument goes to the heart of current HCI views that combine interactionism and embodiment, such as those by Paul Dourish (2004).

The ways in which different users and technologies interact will be no less an issue for positive computing. For example, reminders to engage with one activity may increase distractions for others and reduce chances of engagement and flow conditions. There are surely ways around this, but the need to analyze the impact of design changes holistically is fundamental.

A final approach to supporting motivation and engagement lies in helping each user discover what unique conditions motivate him or her, which can be supported by behavioral analytics, personal feedback, and reflection.

In the next chapter, we turn to the self, looking at the various ways in which technology can support reflection and self-awareness. We also consider how these apparent virtues can flow unwittingly into negative experience such as rumination and narcissism and how we might be guided by a notion that is at once ancient and groundbreaking: self-compassion.

Note

1. The Zombies, Run! game consists of audio recordings and an accompanying website that together weave a zombie apocalypse narrative and elements of game mechanics around your exercise routine. Put simply, you’ll run faster and enjoy yourself more if you’re being chased by zombies. See zombiesrungame.com for more information.

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7 Motivation, Engagement, and Flow

I can hear the threatening moans of the undead gaining on me from behind. Picking up the pace, I break into a run, and my heart quickens. I round a sharp corner, cut through the park, and finally welcome the reassuring news through my earbuds: “Zombies evaded.” I pick up some virtual medical supplies and head for home.

Jogging for your life in the midst of a zombie apocalypse is just one of the many ingenious ways designers have conceived to get people motivated in the modern world.1 The reality is, I’m not the Nike+ type (Dorian, here). I don’t feel like an amazing athlete, not even with a wristband or strategically triggered applause. But immerse me with agency in the unfolding of a satirical suspense narrative, and I’ve managed a heart-pumping run through the neighborhood-all before breakfast.

Motivation and wellbeing intermingle in sophisticated ways. Not only is motivation fundamental to taking any kind of positive action, but the absence of it is a hallmark of depression. Clearly, a life rich in motivation is more rewarding than life without.

Motivation is a trigger to act, and when that activity is sustained by an ongoing urge to carry on, we are engaged. We might be engaged in writing an article, playing Frisbee, or laying bricks to build a house for someone homeless. Our level of engagement may be barely enough to keep us going, or it might be all-encompassing, sweeping us into a state of flow (that place of optimal engagement famously described by Mihaly Csikszentmihalyi.)

It’s hard to imagine a technology designer who wouldn’t be aiming to motivate users in some way, be it to download, upload, collaborate, contribute, click through, or learn more. As such, there are many resources with advice on how to do so more effectively through design. This chapter distinguishes itself by taking the less considered angle of motivation as a contributor to psychological wellbeing. We look at the interrelated notions of motivation, engagement, and flow. We consider key motivation theories, giving special attention to those that provide an explicit bridge between motivation and wellbeing. We then turn to current technologies to seek out hints of how motivation theory might be applied to the design of future things to increase users’ psychological wellbeing.

Motivation

The Pleasure Principle

At the most primitive level, motivation can ultimately be viewed as the desire to seek pleasure and avoid pain. With origins stretching as far back as ancient Greece at least, our understanding of motivation has at various points been contested, reinforced, and expanded by psychologists, economists, and philosophers who have integrated concepts as diverse as altruism, primal urges, autonomy, cognitive dissonance, and interconnectedness into the mix.

Motivational theories, too numerous to cover here, have generally focused on either social underpinnings, psychological drivers (e.g., cognitive), and biological factors. We look at several of the major theories and issues and begin with a point of contention impossible to omit from any discussion of motivation.

Intrinsic and Extrinsic Motivation-a Sibling Rivalry

If I engage in an activity because it’s fun, I am said to be intrinsically motivated. In a sense, the activity is its own reward. If I engage because I fear the stick or crave the carrot, I am said to be extrinsically motivated. The carrot represents a reward separate to the task (e.g., money, points, or approval), and the stick is, of course, a punishment (e.g., exclusion, demotion, or imprisonment), each of which resides outside myself and is controlled by some mechanism external to me (a parent, a boss, or a judge). As Richard Ryan and Edward Deci (2000) put it, “The most basic distinction is between intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something because it leads to a separable outcome.”

Our environment is replete with examples of extrinsic motivators, and most adults now living were educated by a system that relied on it almost exclusively. Nowadays, the mere mention of a stick is a bit cringeworthy and suggests the controversies that have arisen over what motivators are effective, desirable, or just. Although many psychologists and educators have all but abandoned the stick, researchers such as David Greene, Mark Lepper, and Edward Deci have warned that the carrot can be similarly destructive.

In an often cited seminal study (Greene & Lepper, 1974), preschoolers offered a reward to do something intrinsically motivating (draw pictures) lost their intrinsic motivation and drew less than children asked to do it without a reward. This is just one in a slew of similar studies that have exposed the potential for extrinsic motivators to undermine intrinsic motivation, and growth in this area of research has led to a number of modern revisions of how we should structure our workplaces and societies.

Offering contingent rewards (“if you do this, then you get this”) can turn something enjoyable into work, a shift that, over time, degrades intrinsic motivation and may condition us always to need rewards to be motivated. Because intrinsic motivation is associated with quality learning, felt competence, persistence, creativity, positive coping, and wellbeing, then sabotaging it is counterproductive and, some would suggest, a contributor to society-wide problems.

But, obviously, we can’t always be intrinsically motivated to do all that needs to be done in a day, and this is where extrinsic motivators become important. Indeed, Ryan and Deci (2000) argue that certain kinds of extrinsic motivation share many of the benefits of its intrinsic sibling and that the important difference lies in autonomy. They provide a model that may prove invaluable to work in positive computing because it separates extrinsic motivation into four categories, each with a “perceived locus of causality” that is more or less externally derived:

• External regulation, which is entirely “external” (e.g., compliance).
• Interjection, which is “somewhat external” (e.g., seeking approval).
• Identification, which is “somewhat internal” (e.g., activity is valued).
• Integration, which, like intrinsic motivation, is “internal.” In this case, external regulators are assimilated to the self.

For example, a student memorizing a list of pharmaceuticals because she’s desperate to graduate med school and become a doctor is acting in a way that is self-determined, even though the memorizing itself is not intrinsically fun. She has identified the task with a life goal and the causality can therefore be considered “somewhat external.” Someone who volunteers his time to stuff envelopes for Amnesty, isn’t doing it because stuffing envelopes is a blast, but because he is motivated by a compassionate desire to help others and seek justice. The motivation is extrinsic to the task, but highly self-determined and therefore beneficial and rewarding. The task aligns with his core values, and he may have fully integrated this type of activity with himself.

Ryan and Deci’s (2000) review of a number of studies reveals that extrinsic motivation that is more internal (and therefore more autonomous or self-determined) is associated with greater engagement, better performance, higher-quality learning, and greater psychological wellbeing (many of the same benefits attributed to intrinsic motivation).

Social influence such as reciprocity, gratitude, positive self-image, and career goals are all commonly observed motivators for people who engage with social media, all of which can be mapped to Ryan and Deci’s taxonomy of human motivation. In thinking about design for wellbeing, it is useful to know that the range of motivators that are more internally derived will better support wellbeing. Thus, aiming to support more internally derived forms of motivation rather than relying too heavily on simple contingent rewards (or the simplistic imposition of shallow game mechanics) is a worthwhile pursuit in the context of positive computing.

The most obvious deployment of every type of intrinsic and extrinsic motivation in technology design today is mastered in games and seen in the application of game mechanics to nongames, also known as “gamification.” According to information technology and research advisory company Gartner (2011), by the time you read this, “more than 50 percent of organizations … will [have] gamif[ied] [innovation] processes.” Gamification rewards can be used in many ways, including those that undermine intrinsic motivation, but also in effective ways that add motivational layers of enjoyment to inherently unpleasant tasks or as feedback to reflect growing competence. We look at some examples later in this chapter.

Motivation That Is Intrinsic to Being Human

In the case of the volunteer and the medical student mentioned earlier, it may be helpful to look at their goals as stemming from innate human needs, such as purpose, connectedness, competence, and self-actualization (depending on the theory you employ). Although much motivation is contingent on individual interests, some drivers are considered universal to being human. Most obviously there are the physiological drives that urge us to satisfy hunger, protect ourselves from the elements, and procreate. Moreover, many games are built on the natural human motivation to seek patterns in visual information, collect things, connect with others, and resolve cognitive dissonance (sometimes manifest as mysteries, puzzles, or conflicting information). Some of the key theories describing such innate human motivators are described in the next subsection.

Drives, Needs, and Desires

At the foundation of modern motivational theory sits Abraham Maslow’s (1943) hierarchy of human needs. According to this influential theory, we are driven by five levels of needs: “It is quite true that man lives by bread alone-when there is no bread. But what happens to man’s desires when there is plenty of bread and when his belly is chronically filled-” Maslow goes on to answer this question with a list of need categories, each prerequisite to the next, suggesting that a new category is “unlocked” only once the previous category has been reasonably satisfied.

1. Physiological needs, such as food, air, and sleep are primary. Only when these needs are met (and, Maslow argues, they generally are outside of emergency situations) can humans move on to other needs.
2. Safety refers to our need for security and stability as well as to safety from physical danger. Insurance plans, career decisions, savings accounts, burglar alarms, and deep freezers can be looked on as ways in which we are motivated by our need to feel relatively protected from harm and loss. Our desire to accrue, collect, and build things, be they real supplies or achievements and virtual collectables in a game, might be linked to this underlying need for stability and safety nets.
3. Love follows once the first two need categories are fairly well satisfied, and it includes belongingness and the giving and receiving of affection. Various types of digital environments allow us to develop a sense of belongingness to a group, connect with people whom we love or may come to love, and share in affection. (Sending an intimate text message, “poking” someone, or using the kisses emoticon in a chat box can be interpreted as virtual signs of affection (or “affiliative design,” as mentioned in chapter 6).
4. Esteem or the high evaluation of oneself by oneself and by others is a recognizable need. Maslow points specifically to esteem-related desires for strength, achievement, adequacy, confidence, reputation, independence, and freedom. (We discuss self-awareness, self-esteem, and self-compassion in detail in chapter 8.) Much of the way we measure our sense of worth in the modern world is mediated by technology. Whether it’s in endorsements, profiles, “likes,” or eXperience points, technology has an undeniable impact on our capacity to feel and extend esteem.
5. Self-actualization refers to a person’s tendency to reach his potential, to achieve the most he can become, and to feel fulfilled. As Maslow describes it, “A musician must make music, an artist must paint, a poet must write, if he is to be ultimately happy. What a man can be, he must be. … This tendency might be phrased as the desire to become more and more what one is, to become everything that one is capable of becoming.” New models of open education such as open content and massive open online courses provide opportunities for people to explore creative potentials or pursue mastery. Ideally, positive computing will come to increasingly support people in building human psychological potentials such as mindful awareness, compassionate action, and emotional intelligence.

In the search for fundamental human needs, others have followed Maslow. Steven Reiss (2004) has proposed a set of 16 basic desires, most of which can be filed into Maslow’s hierarchy, with the exception, perhaps, of idealism, power, and vengeance. More recent theories on human motivation have given concepts related to autonomy and competence a more central role, such as Ryan and Deci’s SDT mentioned in chapter 2. Also among the most notable is the work of Carol Dweck.

"I Think I Am" versus "I Think I Can"-Fixed and Growth Mindsets

In her early work, Stanford University psychologist Carol Dweck (Dweck & Leggett, 1988; Dweck, 2006) identified two types of behavioral responses: a “helpless” pattern in which people tend to avoid challenges, view obstacles negatively, and reduce performance; and a “mastery-oriented” pattern observed among people who seek challenges and who are persistent in the face of obstacles. In her book Mindset (2006), she describes these patterns of behavior as being related to “fixed” and “growth” mindsets respectively.

Simply stated, those with fixed mindsets believe their abilities arise from innate capabilities and intelligence endowed at birth that cannot be changed. In contrast, those with a growth mindset believe their abilities are developed over time and can be enhanced, a view that is in far greater alignment with recent discoveries on neuroplasticity and epigenetics. It turns out that these two subtle variations in how we view ourselves lead to striking differences in behavior and wellbeing.

According to Dweck’s research, those harboring a growth mindset are significantly better at identifying their strengths and weaknesses, and when faced with a setback, they tend to look for learning opportunities. In contrast, those with a fixed mindset are more focused on judgments. When a fixed-mindset person is confronted with a setback, her tendency is to judge herself, as in “I am a failure.” Even when outcomes are positive, there is a tendency to compare herself to others: “I am better than the others.” Research has shown that the fixed mindset is more likely to lead to unhappiness.

Cognitive-behavioral therapy helps people detect helpless patterns and is one of the most successful modern strategies for helping people with depression. The work of Dweck and others such as Heidi Grant, a motivational psychologist at Columbia University’s Motivation Science Center, have been influential to practices in education, management, and personal development. Some applications of this work include advice to parents and caregivers to praise children for effort and improvement rather than innate intelligence (“Good job, you’ve really improved” or “You worked hard for that” rather than “Good job, you’re so smart” or “I bet you are the smartest in your class”).

These findings can also inform the design of applications that help people set and follow up with goals. Goal-setting tools span a broad spectrum, including those that draw explicitly on psychological theory as well as those that focus more on technical sophistication. What Dweck’s research shows us with regard to wellbeing is that it’s not just the goals you set that are important, but how you think about them and about yourself in relation to them. Therefore, goal-setting tools can impact wellbeing not only by supporting wellness-related goals (I vow to run more or eat better), but also in terms of how they support thinking around goal setting. Trash talk, leaderboards, task breakdown, and deadlines are all approaches to motivating someone toward reaching a goal, and these strategies may or may not have positive impacts on wellbeing depending on the context. Goal setting has been shown to be highly effective in many contexts, but it is not entirely free from caveats when it comes to wellbeing.

Goal Setting-Implications for Wellbeing

Goal-setting theory has provided a framework for investigation into how and under what circumstances defining goals influences things such as performance, self-efficacy, and satisfaction, particularly within organizational and educational settings. Explicit goals are used as motivators, and researchers such as Edwin Locke and Gary Latham have consistently found that setting specific, difficult goals leads to higher performance by individuals and teams. As such, goals are often used, especially in workplaces, to boost productivity and achievement. However, Lock and Latham’s research also shows that the effectiveness of goal setting is dependent on contextual factors such as task difficulty, competence, framing, and self-efficacy. They discuss some of these moderators of goal effects in their essay “New Directions in Goal-Setting Theory” (Locke & Latham, 2006).

Others have examined the effects of goal setting, goal attainment, and disappointment in the area of personal development and mental health. At a broad level, research has found that “individuals valuing relationship goals above achievement oriented goals have been found to have a greater sense of wellbeing than individuals placing achievement goals above relationship goals” (Street, 2002). Helen Street also reviews how goals can either worsen or improve depression and how variables such as goal definition, content, type, and framing play a part. She also explores those situations in which people relentlessly pursue goals regardless of personal cost.

Some have questioned what they consider to be the overuse of goal setting, particularly in American culture. D. C. Kayes (2004) used the 1996 Mount Everest disaster (in which six expert climbers climbed to their deaths despite all safeguards and information urging them to return) as an example of when relentless adherence to an original goal can become irrational and cause team breakdown. “In the face of an environment that requires learning, short-term project teams may encounter the limits of the positive effects of goal-setting.”

More research is needed before we can fully understand the effects of goal setting on practices such as mindfulness and positivity. Mindfulness is frequently described as a method for letting go of plans for the future and nonjudgmentally settling into the realization that “just being is enough.” Goal setting seems decidedly antithetical in this context. Yet the current standard for most wellness technology is to apply tracking features that can encourage both goal setting and comparison to others (“I will practice mindfulness every day this month” or “I failed to be grateful 10 times this week” or even “I can’t believe my brother beat me on meditation today”). For certain types of positive-computing technologies, it may be the relinquishing of goal setting that requires support, at least for some user groups and particularly because many of us currently live in otherwise heavily goal-driven societies.

Clearly, technology designed to promote motivation and wellbeing through goal setting must keep in mind the need for balance and be skeptical of overly simplistic views of goal-setting psychology. As always, a technology team’s greatest safeguard is collaboration with mental health professionals.

There is still much investigation to be done around the effects of various types of tracking, goal setting, self-evaluation, and game mechanics with regard to their roles in appropriate balance-for example, balance between positive thinking and heartfelt authenticity, between directedness and present-moment attention, between drive and calm, desire and contentment, dissatisfaction and acceptance, and other balance relationships related to goals that impact psychological wealth and wellbeing.

Social Motivators

Another motivator intrinsic to being human lies in our tendency to be influenced by the actions and opinions of others. Social psychologist Erving Goffman (1959) proposed that much of human behavior is motivated by how we would like others to see us, and his theory provides a way of conceiving the public versus private lives we keep online. Goffman’s interactionist theory was based on his “dramaturgical approach,” in which behavior is seen as a series of minidramas in which individuals perform in front of an audience, and both are participants in a performance. The performance has a front stage on which the audience sees the individual and a backstage representing where the individual is when alone. Some have described Goffman’s work as “social phenomenology” (Miles & Huberman, 1994).

Sunny Consolvo and Katherine Everitt (2006) propose a set of guidelines based on Goffman’s and Leon Festinger’s (1957) theories for designing systems that encourage physical activity: (1) give proper credit; (2) provide history, current status, and performance measures; (3) support social influence (i.e., use social pressures and support); and (4) consider practical constraints. These guidelines have since contributed to successful systems (Consolvo, 2009a) as well as to more generic and theory-driven guidelines for behavior change (Consolvo, 2009b).

Consolvo (2009b) argues that technologies that support behavior change should support impression management, the individual’s movement between Goffman’s “front-stage” and “back-stage” behaviors. She urges that these technologies need to allow users to manage backstage access. For example, if a user wants to misrepresent an event or conceal an action, the system should support this type of behavior. (Not everyone wants his or her personal foibles or failed exercise routines made public.) This requirement offers social affordances that are common in day-to-day life (we don’t advertise these in person either).

The combination of Goffman’s work and cognitive dissonance theory (Festinger, 1957) provides a useful framework for the design of behavior-change applications, but they do rely on some important assumptions-for example, that our self-control goals always increase our chances of goal achievement. Yet we all have experience with some level of rebelliousness. One of us (Dorian) believes that if a phone were to tell her to put down that brownie because the digital scale sent data to say that she was getting fat, she would probably break the phone and eat two brownies. Work on “ironic processes” (Wegner, Schneider, Carter, & White, 1987) has demonstrated that when trying hard not to think about something, people will think about it more.

Moreover, as mentioned in the chapter on positive emotions, denying negative feelings or enforcing positive thinking via affirmations can have damaging effects to wellbeing. Moreover, there will be cases in which some people’s personal health goals are self-destructive (as in the case of those with anorexia, for example). As part of future research in personal informatics, behavior change, and positive computing, we will need to work on better understanding such complicating issues and their relationship to design and to find ways to devise designs that favor balanced and holistic approaches.

With regard to social influence as a motivator, many current-day apps and websites supply features that allow users to share their milestones and other personal data with others as a way of leveraging the motivational effects of social support and pressure. Of course, the impact of social pressure is not simply always good or always bad. Clearly, the effects of, say, trying to quit smoking may be different depending on whether you are doing it privately or publicly with your friends watching. Peer pressure is notorious in its connotations regarding teen behavior, such as drug use and risk taking. More generally, research by Sonja Lyubomirsky and others (Lyubomirsky & Ross, 1997; White, Langer, Yariv, & Welch, 2006) have highlighted the negative correlation between social comparison and happiness. Furthermore, studies by social psychologists have shown how negative influences can spread in a population (Christakis & Fowler, 2007).

At the same time, a multitude of successful mental health and wellbeing programs, including Alcoholics Anonymous, coming-out programs, and the SuperBetter resilience app, encourage the participant to connect with a sponsor or ally. Positive role models, mentors, and social proof can be just as productively influential as the negative variety can be detrimental. An ongoing research and practice question for positive-computing researchers will be: How can we design to promote positive social influence, while preventing the social validation and spread of destructive patterns-

The Delicate Issue of Persuasion

The psychological research on motivation described earlier makes it clear that motivation is a complex and multidimensional construct. Yet as technologists we often seek simplified models to facilitate practical design work. For example, B. J. Fogg’s (2009) behavior model aims for simplicity in the name of practical application and suggests that humans are motivated by pain/pleasure, hope/fear, and social acceptance/social rejection and that designers can boost motivation by manipulating these. This model underpins work in persuasive technology, an area that Fogg describes as “the automation of behavior change.”

The Fogg model is used for diagnosing interaction design problems, uncovering marketing opportunities, and encouraging small daily habits. Of course these are not the same as supporting psychological wellbeing, so we would be unwise to try and transplant Fogg’s model blindly to the positive computing context. For example, highlighting motivators such as social acceptance and fear in ways that exploit low self-esteem or that contribute to constant anxiety (e.g., “Get rid of unsightly belly fat!,” “We are under attack!”), although effective for many of the applications of persuasive technology, can have serious consequences for wellbeing. Furthermore, in positive computing we need to consider different types of motivation (e.g., extrinsic versus intrinsic) because research shows that motivation as it relates to wellbeing is not one size fits all.

Researchers are working to apply ethical guidelines to persuasive technology (as mentioned in chapter 4) and from the perspective of positive computing, we can view the issue as related to impact on wellbeing. There are various types of motivation and different ways of appealing to them, some of which improve wellbeing and some of which don’t. In general, if wellbeing is our aim, as technologists we must be wary of basing our efforts on a simplified view of human beings and thus risking harm. This risk is one of the reasons we argue so strongly for multidimensional evaluation and for partnerships with wellbeing psychologists who will have a broader knowledge of human behavior and of the strengths and limitations of various models.

How exactly we define a range of motivational impact that includes helpful support at one end and manipulation and propaganda at the other will be a point of ongoing professional debate, but surely both transparency of motives and individual autonomy must be central to making the distinction.

As described in chapter 4, there are other approaches to supporting motivation for behavior change. One that has rapidly gained popularity among those working on population-wide wellbeing initiatives goes by the name of the book that popularized it: Nudge.

Nudging Positive Change-Designers as Choice Architects

Nudge theory can be applied to technology design, but it has been more famously positioned as a model for public policy with the distinct aim to improve organizations and society.

In the book that triggered the wave of interest to follow, Richard Thaler and Cass Sunstein (2008) describe their notion of “libertarian paternalism” that underscores the notion of nudging. A nudge, as they use the term, is “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives.” In essence, it’s about designing things such as policies and information in a way that favors healthier decisions, but without removing choice.

It is perhaps a shame that Thaler and Sunstein opted to use the term paternalism (which they openly acknowledge is saddled with negative and oversimplified connotations). For many, this term obscures the fact that Thaler and Sunstein insist on preserving choice and on basing assessments of improved life satisfaction on the individual’s values rather than on the choice architect’s values.

Perhaps the most compelling aspect of their argument is the notion that it is impossible to be neutral. When we design information technology (as is true for determining policy), we must base our decisions on something, and even if that something is no more than a coin toss, our decisions will have impacts on wellbeing. If that is the case, it seems crazy to base design decisions on mere chance when we might inform them with knowledge of what will improve people’s lives. VSD might look at user values and determine ways of designing to improve user goals based on those values. Positive computing will look at psychological wellbeing and how we can make architectural decisions that are less likely to do psychological harm and more likely to do psychological good, without removing choice. What nudging reminds us is that for any work that seeks to improve lives, it is essential that liberty and autonomy are preserved and that notions of improvement are based on evidence, not on designer opinion (that evidence being research in psychology in the case of positive computing).

Engagement and Flow

In order to move from motivation to ongoing action, one must engage in some sort of activity, and, as such, these two factors are generally studied together. Engagement is quite influencable, so we can purposely design user interfaces and technological interventions that aim to engage people; in other words, our hopes for having impact in this area are well founded.

The Rules of Engagement

To have a prior engagement or be engaged means you have committed seriously to some person or purpose. Likewise, when we are engaged in a digital experience, we have committed to it, perhaps not consciously, but nevertheless it has seduced us to spend our valuable time with it. When educators discuss engagement, they’re speaking about the holy grail of sustained student attention and interest. Positive psychologists often use the term engagement to refer to an active participation with one’s community, with society, or with a higher purpose. Although this term can mean a variety of things, in this chapter we use it to refer to that combination of motivated commitment and sustained attention that educators, psychologists, media designers, and other architects of experience so often seek to elicit from their audiences.

Although there is no unilateral agreement on a precise definition or taxonomy of engagement, there tends to be consensus that when someone is engaged in an activity, they are more likely to enjoy it, to produce quality outcomes, and to learn more (Graham & Weiner, 2012). Most of the academic work on engagement has focused on student engagement in school and employee engagement on the job.

Researchers in the learning sciences (Christenson, Reschly, & Wylie, 2012) provide a definition of engagement that breaks it down into four types:

• Emotional engagement is assessed by detecting supportive emotions (e.g., interest) and the absence of negative, withdrawal emotions (e.g., anxiety or boredom).
• Cognitive engagement is demonstrated when the student uses sophisticated rather than superficial learning strategies.
• Behavioral engagement is generally assessed by observing concentration, attention, and effort (e.g., time on task). This type of engagement is the most straightforward to measure automatically-for example, by using computer vision techniques or behavioral analytics based on the digital traces left during online activities.
• Agentic engagement is signaled by the student’s active contribution to her learning experience (e.g., through creativity and innovation).

High levels of engagement are frequently described as being contingent on appropriate challenge, autonomy, and intrinsic motivation. Parvaneh Sharafi, Leif Hedman, and Henry Montgomery (2006) have mapped these attributes onto a multidimensional model they describe as the engagement modes (EM) model. The EM model specifies five engagement modes: (1) enjoying/acceptance, (2) ambition/curiosity, (3) avoidance/hesitation, (4) frustration/anxiety, and (5) efficiency/productivity. These modes are described on three dimensions: evaluation of object, locus of control between subject and object, and intrinsic or extrinsic focus of motivation. In the EM model, flow emerges when the user faces a pleasurable challenge that is also possible to master.

Flow -- the Holy Grail of Engagement

Flow represents a state of total involvement in autotelic (intrinsically motivated) activity because the activity is so engaging it removes one from reflective self-consciousness. It’s hard to imagine a game designer or software architect who wouldn’t be thrilled to hear that their users were entering states of flow. This reason alone has made it worthy of study among technologists looking to design more rewarding digital experiences.

As part of the Oxford Handbook of Positive Psychology, Jeanne Nakamura and Mihaly Csikszentmihalyi (2009), the originator of flow theory, identify a number of requirements of flow experiences, which include perceived challenges that neither overwhelm nor underutilize our skills, clear reachable goals, and feedback that indicates if we are getting close to these goals. When these requirements are met, it is possible to get into “flow,” an experience that combines:

1. Concentration on the present moment
2. Perceived agency over the situation or activity
3. A merging of action and awareness
4. A loss of reflective self-consciousness
5. A distortion of temporal experience
6. Intrinsic rewards

How many technological features are designed to increase productivity, but in ways that become a hindrance to flow experience- (Think of beeping phone notifications.)

Csikszentmihalyi (1997) describes flow as an “optimal experience” and a key to happiness. Thorough treatment of the varied affective and cognitive consequences of flow ranging from increased subjective wellbeing and greater life satisfaction to addiction on the extreme negative end are well beyond the scope of this book but are included in the collection Advances in Flow Research (Engeser, 2012).

Detecting and Measuring Engagement

Both emotional and cognitive engagement occur internally, and, as such, analysis has relied either on subjective reports or on instruments such as the Motivation and Engagement Scale (Martin, 2007), often used in pre/postexperimental designs, measurement studies, and path modeling. Both of these techniques have the limitations associated with after-the-fact reporting (Liem & Martin, 2012).

Flow has been measured through interviews, surveys, and experience sampling. More recently, advances in emotionally intelligent interfaces (Grafsgaard, Wiggins, Boyer, Wiebe, & Lester, 2013) and computer modeling allow us to approach the measurement of engagement constructs in new ways. In particular, sensing- and affective-computing techniques allow for ways of integrating observed expressions of engagement with internal self-reported measures. These techniques use sensors such as video cameras to record voices, facial expressions, and physiology (Calvo & D’Mello 2010). Moreover, we can generalize data from subjective reports in order to detect engagement automatically from a wider number of users more easily and with increasing accuracy.

Kazuo Yano, Sonja Lyubomirsky, and Joseph Chancellor (2012) describe an experiment in which flow was detected by physiological sensors as consistency of movement: “The key indicator of flow turned out to be consistency in movement. For some people, that consistent movement was slow; for others it was fast. Some were morning people; others favored the afternoon or evenings. Regardless, when participants experienced flow, their motions became more regular, as they lost themselves in a challenging but engrossing activity.”

Engagement and Games

When we’re not using our digital devices to engage with learning or work, there’s a good chance we’re using them to play. We’ve been playing games from before we could write (personally and historically), which suggests that games meet basic human psychological needs. One of the great powers of games is their ability to engage us fully. Gaming is an area in which levels of engagement can be particularly high, so digital game research is an obvious place to turn for guidance on engaging users in aid of wellbeing, but also for insight into when engagement becomes addiction and impacts wellbeing for the worse.

So do videogames increase or decrease wellbeing- According to research, the answer is: both. Our challenge is to pull apart the fibers of the digital gaming experience until we can better understand which characteristics improve wellbeing in what contexts and which don’t.

When it comes to books, whether reading improves or decreases your wellbeing depends largely on content. With videogames, content is significant, but there are also critical wellbeing implications for how a game is played and who it’s played with (Johnson, Jones, Scholes, & Colder Carras, 2013)-in other words, the social context and the game mechanics.

Exposure to violent videogames has consistently been shown to increase aggression, desensitize to violence, and reduce prosocial behavior. For example, a recent meta-analytic review (Anderson et al., 2010) concludes that “the evidence strongly suggests that exposure to violent video games is a causal risk factor for increased aggressive behavior, aggressive cognition, and aggressive affect and for decreased empathy and prosocial behavior.”

But not all games are violent, and, unsurprisingly, just as practiced aggression can increase aggression, prosocial games can increase prosocial behavior (Gentile et al., 2009). It’s not just the content that matters, but how you play. Game mechanisms vary both in general and in the details, and we suspect that key wellbeing differences lie in both core game mechanisms and in those easy-to-miss details.

The negative effects of videogames are fairly well publicized, but research on the positive effects of games on both psychological and physical wellbeing is less well-known. A survey of randomized controlled studies (Baranowski, Buday, Thompson, & Baranowski, 2008) on the use of videogame interventions for improving mental and physical health-related outcomes revealed moderate to strong positive results across 38 studies representing 195 health outcomes. These studies used videogames to provide physical therapy, psychological therapy, and improved disease self-management, among other things. Interestingly, positive effects were strongest (69 percent) for psychological outcomes.

Only seven of the studies were psychological interventions, and they included the use of casual, strategy, and custom games for post-traumatic stress disorder, anxiety, age-related cognitive decline, dyslexia, attention capacity, and self-esteem. Furthermore, many more studies in the literature were not included in this survey because they did not meet the randomized controlled trial criteria. The authors conclude that higher-quality studies remain relatively uncommon in this area and that “in order to most effectively assess the potential benefits of video games for health, it will be important for further research to utilize (1) [randomized controlled trial] methodology when appropriate; (2) longer follow-up duration; (3) improved measures of quality, such as randomization and blinding; and (4) standardized measurement tools and careful attention to the quality of outcome measures.” In other words, early results are promising, but more work remains to be done.

Carmen Russoniello, Kevin O’Brien, and Jennifer Parks (2009) showed the ability of casual games to reduce stress, anxiety, and depression. As mentioned in chapter 6, a recent review of digital games (Johnson et al., 2013) looked at their impact on young people’s wellbeing and concluded that they “positively influence young people’s emotional state, self-esteem, optimism, vitality, resilience, engagement, relationships, sense of competence, self-acceptance and social connections and functioning.”

This impact suggests that games are still largely untapped for positive-computing research and design. Of course, in the midst of our enthusiasm, we must still be careful to resist sweeping generalizations. That some studies have shown that some specific casual games can decrease stress and depression is very significant and means we can design for that outcome-but it doesn’t follow that all games or even all casual games do this for all people or that secondary effects may not also emerge.

By way of anecdotal illustration, many of us have probably already enjoyed the stress-relieving qualities of Bejewelled, the popular game that proved therapeutic in the East Carolina study (Russoniello, O’Brien, & Parks, 2009). However, this same game has multiple incarnations, one of which, Bejewelled Blitz, is styled with all the bells-and-whistle rewards of slot machines and gambling. As enjoyable as I (Dorian) found it myself, I was persuaded to remove it from my tablet when my seven-year-old began begging me for money to use on gambling in the game. I shuddered to think it might be conditioning his formative mind to respond all the more rapidly to this kind of manipulation later in life.

The problem for wellbeing in this scenario is not games in general or even the core game mechanism itself (pattern matching), but the way in which this particular version was designed to profit financially from gambling-based incentives. When I replaced it with an equivalent that employs the same core pattern-matching mechanism, but without the gambling layer, Candy Crush, the negative side-effect disappeared.

My humble experience is hardly research evidence, but it is illustrative of how multilayered and complex the short- and long-term effects of even apparently simple casual games will be on wellbeing (as well as of how design for optimum profit can come up against design for wellbeing). Inevitably, some design aspects of any technology may contribute to wellbeing, while others may decrease it in parallel. This is what seems to occur with cooperative violent videogames that have been correlated with aggression but also cooperation skills (Greitemeyer, Traut-Mattausch, & Osswald, 2012; Velez, Mahood, Ewoldsen, & Moyer-Guse, 2012). Playing as a team, users can learn valuable cooperation skills and find new ways to connect, but doing so in the context of enacting graphic violence or as part of a simplistic “us” versus “them” mentality are separate aspects with potentially negative consequences for the wellbeing of individuals and society. Game designer Raph Koster (2013) suggests that our gameplay (digital and otherwise) continues to reinforce instinctive skills once critical for our survival as cavemen but now obsolete, such as shooting and aiming (once important for hunting). He also cites “blind obedience to leaders and cultism, rigid hierarchies, binary thinking, the use of force to resolve problems, like seeking like and its converse xenophobia” as common themes based on obsolete skills. He suggests that we should be designing games that evolve with us and reinforce skills relevant to the modern world. By way of example, he highlights the game Diplomacy, which according to Koster is not only an example of a game that reinforces modern skills, but one that also provides “evidence that remarkably subtle interactions can be modeled within the confines of a rule set and traditional role-playing can reach the same heights as literature in the right hands.”

Game addiction is another serious concern for anyone looking to use games to foster psychological wellbeing, and research has implicated risk factors such as personality traits, motivations for playing, and structural game characteristics (Kuss & Griffiths, 2011). But what if games themselves could be used to build resilience against game addiction while fostering positive engagement- Work uncovering risk factors (Kuss & Griffiths, 2011) and studies on resilience factors (risk of Internet addiction reduced by self-compassion [Iskender & Akin, 2011]) have begun to pave the way.

Although a discussion on addiction is well beyond this book’s scope, the message emerges that if we are to be genuinely effective in leveraging the incredible potential of games for positive computing, we need to work carefully through what will be a slowly unraveling story of psychological impact and an important ongoing area of research. The key to a future of positive games lies in giving these technologies credit for producing highly multifaceted and complex experiences, acknowledging the incredible potential they provide, and exploring all the effects of game design on wellbeing so that we can increasingly favor the beneficial ones.

Design Implications

Designing to Motivate

The Zombies, Run! game, alluded to at the start of this chapter, is one of a multitude of apps and “exergames” whose primary offering is motivation-namely, motivation to do things we don’t otherwise feel intrinsically motivated to do. We may want to do things that improve our wellbeing, but when those things also require effort or are unpleasant in the moment, we are at odds with the pleasure principle. Technology, sometimes via gamification, can step in to resolve the conflict. By layering experience and challenge that are intrinsically enjoyable (e.g., playing a part in a zombie narrative) over the activity that isn’t (e.g., running), if the two activities are sufficiently intertwined (running becomes part of the story), then the whole experience can become more rewarding, thereby increasing our intrinsic motivation to take part.

Uplifted, created by the United Kingdom’s Channel 4 for promoting positive emotions, takes a slightly different approach and embeds moments of positive reflection into an Angry Birds-style casual game. The game and reflection are thematically linked but not intrinsically linked as they occur separately and have little bearing on one another.

Other approaches engage our self-determined extrinsic motivation by helping us to articulate and track goals, be they larger goals (I will eat healthier) or smaller subgoals (I will chug a glass of water every morning) in aid of closing the gap between our behavior and our goals and values.

Designing for Engagement and Flow

In 2006, Yvonne Rogers (2006) discussed designing for increased engagement as an alternative to quiet automation for a future of ubiquitous computing. Rogers proposes “a significant shift from proactive computing to proactive people; where UbiComp technologies are designed not to do things for people but to engage them more actively in what they currently do. Rather than calm living[,] it promotes engaged living, where technology is designed to enable people to do what they want, need or never even considered before by acting in and upon the environment.” Design for human autonomy (as opposed to machine autonomy) will be critical to fostering wellbeing (Calvo, Peters, Johnson, & Rogers, 2014). An important balancing factor for human autonomy (as pointed out to us by Ben Shneiderman in conversation) is the reality of interdependence and our interconnected relationship to others. Both of these are core to SDT. If we were to look at design for motivation and engagement from the perspective of SDT, then when we evaluate a technology, we might ask these questions: Does the user experience respect autonomy- Does the user experience support a sense of competence- Does the user experience support connection to others- Of course, in being a theory of both wellbeing and motivation, it should also help us create conditions for engagement and flow.

Although it is generally accepted that there is no way to reliably design an experience of flow (the triggers are too individual), there are nonetheless ways to design conditions that increase the likelihood of flow experience. One approach to doing this is to identify the obstacles to flow and design to reduce them. Researchers in attentive computing investigate, among other things, how interruptions can be minimized and attention sustained. Modern versions of popular productivity software such as Microsoft Word and WordPress have incorporated options that remove screen clutter, allowing the user to focus only on the task at hand. Similar examples were Apple’s inclusion of the “Do not disturb” setting in its iOS, and Freedom, a software application whose sole purpose is to allow you to easily shut down your Internet connection for a set period of time so you can proceed without distractions.

Another approach to supporting flow is to identify those conditions that are particularly conducive to it and include them where possible. For example, based on the work by Nakamura and Csikszentmihalyi (2009), we should design for appropriate challenge, clear reachable goals, and feedback that indicates if we are getting close to these goals. Certainly, these conditions are already familiar to game and interaction designers. A third approach to designing for flow, rather than looking at the universals, honors the individuality of what triggers flow for different people. In a study mentioned earlier (Yano et al., 2012), the research team used sensor data to help workers determine what time of day they were most likely to get into flow. They could then adjust their schedules and habits accordingly.

A significant characteristic of flow theory for HCI is its interactionism (a focus on a system made of the person and the environment). Csikszentmihalyi (1997) describes an emergent motivation that arises from the interaction that forms the context of a flow experience rather than a motivation that is a property of the individual (i.e., commitment) or of the environment (e.g., persuasion). Such a line of argument goes to the heart of current HCI views that combine interactionism and embodiment, such as those by Paul Dourish (2004).

The ways in which different users and technologies interact will be no less an issue for positive computing. For example, reminders to engage with one activity may increase distractions for others and reduce chances of engagement and flow conditions. There are surely ways around this, but the need to analyze the impact of design changes holistically is fundamental.

A final approach to supporting motivation and engagement lies in helping each user discover what unique conditions motivate him or her, which can be supported by behavioral analytics, personal feedback, and reflection.

In the next chapter, we turn to the self, looking at the various ways in which technology can support reflection and self-awareness. We also consider how these apparent virtues can flow unwittingly into negative experience such as rumination and narcissism and how we might be guided by a notion that is at once ancient and groundbreaking: self-compassion.

Note

1. The Zombies, Run! game consists of audio recordings and an accompanying website that together weave a zombie apocalypse narrative and elements of game mechanics around your exercise routine. Put simply, you’ll run faster and enjoy yourself more if you’re being chased by zombies. See zombiesrungame.com for more information.

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