The Behavior Change Design cards are now available at persuasive.ac.cy/bcdcards. For more information on their creation and evaluation, you may read the full paper at the link below:

• Konstanti, C., Karapanos, E., & Markopoulos, P. (2021). The Behavior Change Design Cards: A Design Support Tool for Theoretically-Grounded Design of Behavior Change Technologies. International Journal of Human–Computer Interaction, 1-17.

Our interactions article summarised our recent work on nudging: the ’23 ways to nudge’ framework and the Nudge Deck. Read the article here.

The Nudge Deck is now available to download from the following link: http://persuasive.cut.ac.cy/nudgedeck

We recently published an article summarizing our work and our vision for a methodological paradigm we call Technology-Assisted Reconstruction. You may be asking: what is Technology-Assisted Reconstruction?

Suppose you want to measure how people feel, or what people do throughout the day. In Human-Computer Interaction (HCI), you may be interested to study how a novel communication system affects feelings of connectedness among family members, or how a self-tracking technology affects users’ motivation for, say, physical exercise. How do you go about it?

One approach is to ask people at the end of the day to recall episodes where they interacted with the system and recount how they felt and what they did during those episodes, or to ask them to provide “global reports” on how they system affects their feelings of connectedness or their motivation for physical activity, overall. Such retrospective judgments suffer from recall and social biases.

Another alternative is to employ the Experience Sampling Method (ESM), which consists of prompting the user with short questionnaires, at random, or otherwise-defined times throughout the day. With the widespread adoption of sensor-filled mobile technologies, we can now schedule such prompts rather effectively. For instance, we can ask people to report how motivated they feel, right after they interact with the self-tracking technology, thus being able to study the proximal impact of feedback on individuals’ motivation. ESM minimizes recall and social biases and is considered to be the gold standard of momentary assessment.

One challenge with ESM, is that it imposes a high burden on participants due to the repetitive interruptions. Daniel Kahnneman and colleagues published an article in Science in 2004 on an alternative to ESM, the Day Reconstruction Method (DRM). DRM does not interrupt participants throughout the day but rather asks participants, at the beginning of the next day, to list their daily activities as a continuous series of episodes. This is assumed to minimize recall bias through increasing the number of cues to episodic memory people have access to, and which they use to infer experiential information. DRM has been found to provide a reasonably good approximation to experience sampling data and the method has been well adopted by the HCI community.

With Technology-Assisted Reconstruction we argue that our field has the capacity to contribute towards a next step in the field of momentary assessment, where passively logged data from sensor-based technologies, such as the locations we visit, the social interaction we perform through technology, and what we see, as captured from lifelogging cameras, can be leveraged to support recall. This article attempts to make three contributions:

• It reviews the use of ESM and DRM in HCI literature and discusses the challenges researchers have encountered.
• It presents our vision on Technology-Assisted Reconstruction and illustrates this, through five different methods we have developed over the past ten years.
• It proposes a framework for the development of TAR methods (see table 2), which summarizes three distinct strategies: (a) enhancing the encoding process as the event takes place, (b) guiding the recall process, and (c) providing external memory cues during recall.

Karapanos, E. (2019): Technology-assisted reconstruction: a new alternative to the experience sampling method, Behaviour & Information Technology, DOI: 10.1080/0144929X.2019.1608303

 

I intentionally chose a provocative title. Can we really design behaviour? Can technologies have such an influence on individuals’ behaviour that we can talk about “designing behaviour” through technology?

To a large extend, behaviour change technologies nowadays rest on the assumption that knowledge leads to behaviour change, an idea that is deeply rooted in the “quantified self” community. We assume, for instance, that making people aware of how much, when, and where, they walk (or don’t), will lead them to uncover patterns in their behaviours and take actions to increase their physical activity. Our research has revealed that this is not necessarily true, and that we often hold incorrect assumptions about how people use these technologies. For instance, our habito study, revealed that people rarely look back at their past performance data, and that only 30% of the users set their own walking goal.

Rather than thinking of such technologies as ones that enable deep reflection over one’s behavioural patterns, could we have more success if we focus on shaping the choice architecture at moments of decision making? Consider, for instance, the popular road sign stripes that are strategically positioned closer and closer together as we approach a steep curve, making us believe that we are over-speeding. Such a simple intervention has been found to decrease car accidents by 36%.

How could we transfer such approaches to the design of behaviour change technologies? I personally see  two approaches. One is to infer when such critical moments of decision making happen – approaching the elevator, browsing through online food delivery options etc. A second is to provide persistent, glanceable feedback that keeps reminding people of their goal. Consider, for instance, that people glance at their smartwatches 80-150 times a day to check the time or incoming notifications. Might we leverage on this to present physical activity feedback at the periphery of their attention?

In our recent paper we explored just that. For the purpose of this blog article, I wanted to emphasize what we found with one of the prototypes we developed, TickTock.

Most activity trackers will tell you how much have you walked so far in the course of the day. This, of course, is routed in the idea that people set daily walking goals, and feedback serves to make them aware of their performance so they can meet their goal.

With TickTock, we tried to change the logic.  TickTock will only tell you how active you were over the past hour. As a result, TickTock emphasises keeping a balance in your day, i.e. avoiding prolonged periods of sedentarism, which has been found to be a health risk factor independently of the amount of physical activity one performs over the course of a day.

What we found was rather astonishing, I think. I initially thought there must some error in the data. I asked Ruben to scrutinise the data collection script. He couldn’t find any error.

I turns out, TickTock had a profound impact on individual’s behaviours. We performed a linear regression analysis to predict the time people took till their next walk after checking the watch, based on the feedback they received, namely how active (o-60 mins) they were over the past hour. We found this single variable to account for 76% of the variance in the data! For every additional 10 min of physical activity that the participants saw they performed over the past hour, they would take an extra 9.5 min till their next walk. Participants who saw that they walked 10 or less min over the past hour had a 77% probability of starting a new walk in the next 5 min.

What do those findings suggest? First, that these technologies are far from neutral, they emphasise certain ways of living, and as designers, we should pay close attention to the assumptions embedded in our designs and to align feedback with the goal we want to achieve. Second, they highlight they surprising impact glanceable feedback has on individuals behaviours. The habito study highlighted that the dominant mode of use of physical activity trackers is glancing and it serves to support self-regulation of immediate behaviour. This study further demonstrated the power of glanceable feedback in shaping individual’s behaviours.

Gouveia, R., Pereira, F., Karapanos, E., Munson, S., Hassenzahl, M. (2016) Exploring the Design Space of Glanceable Feedback for Physical Activity Trackers, In Proceedings of Ubicomp’16.

As HCI shifts “to the wild”, in-situ methods such as Diary Methods and the Experience Sampling Method are gaining momentum. However, researchers have acknowledged the intrusiveness and lack of realism in these methods and have proposed solutions, notably through lightweight and rich media capture. In this paper we explore the concept of lifelogging as an alternative solution to these two challenges. We describe Footprint Tracker, a tool that allows the review of lifelogs with the aim to support recall and reflection over daily activities and experiences. In a field trial, we study how four different types of cues, namely visual, location, temporal and social context, trigger memories of recent events and associated emotions. We conclude with a number of implications for the design of lifelogging systems that support recall and reflection upon recent events as well as ones lying further in our past.

Gouveia, R., and Karapanos, E. (2013) Footprint tracker: supporting diary studies with lifelogging. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’13). ACM, New York, NY, USA, 2921-2930.

Technologies for behavior change have immense potential. Consider, for instance, the case of physical activity trackers. Our healthcare systems are facing unprecedented challenges. Western lifestyles, now spreading throughout the world, have had a direct impact on the increase of chronic diseases, which today account for nearly 40 percent of mortality cases and 75 percent of healthcare costs, and are predicted to increase in frequency by 42 percent by 2023. Obesity alone has been estimated to account for 12 percent of the health-spending growth in the U.S. It is thus no surprise that policy makers and political figures are increasingly calling for a healthcare model that stresses patient-driven prevention rather than cures, such as Hillary Clinton’s call for a health initiative that focuses on “wellness, not sickness” and Gordon Brown’s call for an “NHS [National Health Service] of the future [being] one of patient power, with patients engaged and taking control over their own health and healthcare.”

In this new landscape of healthcare, physical activity trackers have become a focus in both research and practice, as they can provide many benefits ranging from empowerment and people taking responsibility for their own health to opportunistic engagement in desired behaviors [1]. The market for wearable activity trackers such as Fitbit, Jawbone up, and Nike+ Fuelband has seen a rapid growth, estimated to have grossed $1.15 billion in 2014.

Karapanos, E. (2015) Sustaining User Engagement with Behavior Change Tools,      Interactions 22, 4 (June 2015), 48-52.

Sustaining User Engagement Is Challenging

Despite significant recent advances, one could argue that research and practice in behavior-change technologies are still in their infancy. The industry is currently following a technology push paradigm, appealing to the user’s interest in experimenting with self-quantification. In research, our efforts are continually expanding to different sensor technologies and different uses of them, as if we have actually been successful in developing behavior change strategies that work.

The reality of behavior change technologies, however, is somewhat disappointing. A recent survey found that over a third of owners of commercial physical-activity trackers discarded them within six months [2]. The reality is even worse for activity-tracking mobile apps, where adoption is often more exploratory, and for researchers who typically deal with exploratory ideas and prototypes that are necessarily less developed than commercial products. For instance, in our own work we found that out of the 86 users who installed a physical activity tracker that we deployed on Google Play, only 21 percent of them kept using it for more than two weeks [3].

Ensuring long-term engagement with behavior-change tools is important for a number of reasons. The majority of today’s behavior-change technologies rely largely on the principle of self-monitoring—the idea that monitoring our behaviors makes us more likely to engage with behavior change, be it walking the extra steps, reducing our energy consumption, or other changes. Research has repeatedly shown, however, that individuals quickly relapse into their old habits once self-monitoring ceases.

Strategies for Sustaining User Engagement

The question we pose in our research is how to sustain user engagement with behavior-change technologies (and the behavior-change process per se) over a prolonged period of time. We now describe some of the different paths we have explored so far.

Creating checking habits. The question we are asking here is: How can we design behavior-change technologies that entice users to keep checking their data?

Recent work has highlighted the capacity of smartphones to create strong checking habits —“brief, repetitive inspection[s] of dynamic content quickly accessible on the device” [6]. Powered by instant information rewards found most commonly in social media updates and incoming emails, these brief interactions, lasting fewer than 15 seconds, have been found to account for as much as 40 percent of our usage sessions with smartphones.

On one hand, the potential for checking habits to transform into addictive behaviors is alarming. On the other hand, checking habits can also serve a good purpose—for instance, if they sustain user engagement with an application that helps them to exercise more.

Habito does this through the use of textual feedback. We  created a total of 91 messages, which are displayed to users over time and when certain conditions are satisfied. Some of these messages aim to support further inferences about the activities performed. For instance, when the system has sensed high physical activity at a given location, it colors this entry as green, and the text below may provide further detail, for example: “M-ITI has been your most active location of the week. On average, 400m more than other locations” and “In your breaks at M-ITI, you walked an average of 50 meters.” Others provide mere facts, such as “Only 13% of children walk to school nowadays compared with 66% in 1970” or “Keep active. Simple movements such as fidgeting, which includes knee shaking or pen tapping, can burn up to 3,600 calories per day.” Others provide just-in-time recommendations such as “You have been sitting for 45 minutes. Try taking a break every 30 minutes,” when the system has sensed extended sedentary activity, or “If you have time, park your car further away and walk the remaining distance!” when the system has sensed commuting by car. Others try to create a sense of community, for example, “O’Calhau is the 2nd most physically active community in Funchal. Just 300 meters below the first (M-ITI).”

Our hypothesis was supported. We found that users take less time to come back to Habito following an interaction with a novel message compared with a message they had seen before. Furthermore, interacting with persuasive messages as opposed to merely informational messages would make them take a walk in a shorter period of time.

Social translucence. The second strategy suggests: If persuasive technologies are not successful in the long run, what if we push the responsibility to families and other strong social ties? This implies a change in perspective. Rather than thinking of such technologies as persuasive, we think of them as socially translucent, where the goal is to raise awareness of one another’s behaviors. Behavior change in this sense is expected to happen not as a result of the technology, but rather because of the often-ingenious forms of nudging families employ (e.g., a mother putting some tape over the light switch so her children don’t use it every time they come into the room).

While social influence is one of the most common approaches employed both by research prototypes and commercial products, it is often reduced to simplistic techniques (such as competition) that are mediated only through the technology. In our work we attempt to reduce the role of technology in the behavior-change process to two important attributes: visibility—allowing information about the individual’s behaviors to be seen by others—and mutual awareness—raising the individual’s awareness that this information is visible to others (“I know that you know”). The combination of these two attributes is expected to lead to compliance and feelings of accountability regarding one’s behaviors [7]. Most important, technology’s role is reduced to one of mediation; it is the communication and coordination practices that families establish that define the path to behavior change.

Consider for instance Social Toothbrush, a prototype developed by Ana Caraban as part of her master’s thesis (Figure 3). The prototype senses the frequency, duration, and performance of the user’s toothbrushing practices and provides information both in real time and during subsequent use, as does any other personal informatics tool. More important, a minimalistic glanceable display (in the form of a colored light in the lower part of the prototype) allows a person entering the bathroom to know whether the toothbrush has been used during the past couple of hours. Similarly, Donovan Costa in his master’s thesis developed Hydroscale, a prototype that senses the user’s water-intake practices in the workplace and reminds them when it’s time to have another sip. The fact that the display is visible to everyone in the vicinity plants the seeds for playful nudging and discussion over the user’s water intake. One may as well choose to adhere to recommended water-intake practices to avoid this form of nudging. This strategy certainly poses significant ethical questions: Is it likely to raise conflicts within families or between individuals, or is it the first step to the creation of norms and adherence to them, eventually even leading to a possible reduction in conflicts?

Supporting action. While today’s physical-activity trackers and behavior-change technologies all inform users about their levels of physical activity, they do little to assist them in implementing new habits. CrowdWalk tries to combat this by proposing walking activities one can do from the current location. Activities are produced by users and can be tied to breaks (e.g., “walk around the campus—it will take you 15 minutes”) to commuting (e.g., “walk downtown rather than taking the bus”) or to practices that involve walking (e.g., “try to maximize the distance walked while shopping in the supermarket”). While CrowdWalk proposes walking activities to users, it doesn’t nudge them to carry out these activities (see [8] for a different approach). While this avoids , it suffers from possible attrition as individuals forget or lose interest in the proposed activities. How to assist users in transforming these walking activities into daily habits is a key question for such tools.

Strategies for Sustaining User Engagement

We proposed three directions, but also considerations, for the design of behavior-change technologies that sustain users’ engagement.

The first direction implies that behavior-change technologies have to fight for users’ attention. In a life of constantly fragmented attention filled with distractions and changing priorities, how do we design technologies that do not overwhelm the user, and yet do not disappear in the muddle of daily life?

The second direction implies that social relationships may be more effective in inducing behavior change than human-technology ones. The question posed to us designers is how to integrate humans and technologies in supporting the behavior change process in a way that is ethical, effective, and socially acceptable.

The third direction implies that assisting behavior change is not only about reminding people, but also about telling them how. Keeping to a healthy diet is far easier when a clear, step-by-step process is available. As designers, we should ask, does the technology suggest clear actionable goals that are tailored to the user? Does the technology have the agency to stress the implications of these goals?

Last but not least, we suggest that engagement is key to behavior change. In fact, the majority of early studies on behavior-change technologies overlooked users’ engagement, focusing directly on the final outcome: whether behavior change took place or not. We argue that understanding how people engage with behavioral feedback is an important gateway to the invention of more effective behavior-change strategies. For instance, in a recent study we found that contrary to conventional wisdom that portrays behavior change as the result of deep knowledge about one’s own behaviors, users truly lack the interest to reflect on past behaviors. Instead, more than 70 percent of the long-term usage of our tracker related to glances—brief, five-second sessions where users called the app to check their current activity levels, with no further interaction [3]. Consequently, we should question the current paradigm of deep reflection underlying most of today’s technology. Instead, we should ask: How can we maximize the impact of these glances on individuals’ behaviors, and how can we leverage these glances to act as proxies for deeper engagement with the feedback?