On reflection, though, I believe that our progress here will shortly present us with a substantial new challenge: what do we do when an individual’s preferences are different from what actually works for that individual?

From all sorts of research, we know that people are not very good at predicting what features they actually want over the long term or what will work for them. It’s why Halko and Kientz’s work on personality differences and individuals’ receptiveness to different proposed persuasive systems is great, but the actual efficacy needs to be tested in the field. It’s why people who viewed prototypes of our GoalPost app loved trophies and medals in theory, but found them unmotivating — or even demotivating — in actual use.

This probably isn’t a problem when designers use detected or reported personality differences to make small adjustments to messages within an application. It does, however, present a dilemma if people choose applications with bundles of features that are appealing to them but that do not actually help them achieve their goals, or that help somewhat but not as nearly well as a different bundle of features would. Rosa Ariaga brought this issue to mind at Pervasive Health by asking “why don’t we just create a completely customizable app, and let people choose the features that will motivate them?” My reaction was that, oh, gosh, it seems like many would pick the wrong features and, rather than adapting, just get more discouraged.

Thus, it seems like there are additional questions that should be part of the agenda for people working on individual differences and persuasive systems:

• What personality (and other) attributes predict different individual preferences for adopting persuasive systems, and what attributes predict individual differences in efficacy of persuasive systems?

When do people pick systems that are well matched for their actual needs, and when do they pick systems that are poorly matched?

• What can, or for that matter, should designers of systems do when people are inclined to pick systems that are not actually helpful for them while neglecting systems that would help?

Consider the enticement, to the right, which appeared outside of all of the talk rooms. So, once I get past the flyer yelling at me, I learn that I can earn my way toward useless swag by thanking a presenter. There were several like this — thank a committee member member, or “find an entry in a student competition and talk to the students about their experiences” — things that are good to do as a member of the community, and for which there are (or should be) sufficient intrinsic motivators.

But now? Now your thanks are just a tool to get stuff for yourself. And as presenter or committee member, you’re left wondering if a thanks is sincere or just because someone needs to check off another badge on their way to status or a chotsky. Sigh.

About the only good thing I can say about it is that it didn’t appear to catch on. If you’re organizing CHI 2013 or another conference: please, let’s not have any more of this BS.

It is, nevertheless, important. First, there may be times when designing systems to support temporary use may actually help some of the initial adoption and adherence problems: people might be willing to put up with a tedious process or a somewhat intrusive device if an application promises to teach them new skills and then be gone from their lives. Second, if we consider what it is like to live with persuasive systems, how many of us would want people to have lives that are carefully regulated and nudged by a myriad of systems, until the day we die [3]? And finally, might some persuasive health systems create an effect of learned helplessness in which applications, assuming the role of determining and recommending the most appropriate choices, actually reduce individuals’ competency to make these decisions in the absence of that support?

Anecdotally, many researchers have described high recidivism rates after the conclusion of an intervention, when the fitness sensor or diary, or the calorie counting tool, is no longer available to the former subjects (this has been observed with other types of interventions as well [4]). Why are these applications not helping individuals to develop good, robust fitness habits or competencies for health eating and at least keeping approximate track of calories? Would a study actually find worse post-intervention health habits among some participants?

To help imagine what we might build if we had a better understanding of how to create temporary health and wellness interventions, consider Schwanda et al’s study of the Wii Fit [5]. Some stopped using the system when it no longer fit into their household arrangement or routine, others when they had unlocked all of the content and its activities became boring or repetitive, and others stopped using it because they switched to another, often more serious, fitness routine. From a fitness perspective, the first two reasons might be considered failures: the system was not robust to changes in life priorities or in living space, or it suffered a novelty effect. The third, though, is a fitness success (though possibly not a success for Nintendo, if the hope is that they would go on to buy the latest/greatest gaming product): participants “graduated” to other activities that potentially were more fulfilling or had still better health and wellness effects. Imagine if the design of the system had helped more users to graduate to these other activities before they became bored with it or before it no longer fit into their daily lives.

Returning to the examples of exercise and calorie diaries, what changes might make them better at instilling healthy habits? In the case of a pedometer application, could it start hiding activity data until participants guessed how many steps that had taking that day? Would such an interface change help people learn to better be aware of their activity level without a device’s constant feedback? What if, after some period of use, users of calorie counters started not getting feedback on the calories they had consumed per food until they end of the day? Would such activities support development of individuals’ health competencies better than tools that offer both ubiquitous sensing and feedback? How would such changes affect the locus of control and sense of self-efficacy of applications’ users?

These are some rough ideas – the medical community, perhaps because of a focus on controlling costs and/or lower ability to integrate the interventions they design into daily life, has more history of evaluating interventions for the post-intervention efficacy (e.g., [6], [7]). Other communities have deeper understanding of what it takes to develop habit (e.g., [8], [9]) or to promote development. What does the HCI community stand to learn from these studies, and to what extent should or community conduct them as well?

1. Munson SA, Consolvo S. 2012. Exploring Goal-setting, Rewards, Self-monitoring, and Sharing to Motivate Physical Activity, Pervasive Health 2012. [pdf] ↑
2. Munson SA, Lauterbach D, Newman MW, Resnick P. 2010. Happier Together: Integrating a Wellness Application Into a Social Network Site, Persuasive 2010. [pdf] ↑
3. Purpura S, Schwanda V, Williams K, Stubler W, Sengers P. 2011. Fit4Life: The Design of a Persuasive Technology Promoting Healthy Behavior and Ideal Weight, Proceedings of CHI 2011. [pdf] ↑
4. Harland J, White M, Drinkwater C, Chinn D, Farr L, Howel D. 1999. The Newcastle exercise project: a randomised controlled trial of methods to promote physical activity in primary care, BMJ 319: 829-832. [pubmed] ↑
5. Schwanda V, Ibara S, Reynolds L, Cosley D. 2011. Side effects and ‘gateway’ tools: advocating a broader look at evaluating persuasive systems, Proceedings of CHI 2011. [pdf] ↑
6. Bock BC, Marcus BH, Pinto BM, Forsyth LH. 2001. Maintenance of physical activity following an individualized motivationally tailored intervention, Annals of Behavioral Medicine 23(2): 79-87. [pubmed] ↑
7. Moore SM, Charvat JM, Gordon NH, Roberts BL, Pashkow F, Ribisl P, Rocco M. 2006. Effects of a CHANGE intervention to increase exercise maintenance following cardiac events, Annals of Behavioral Medicine 31(1): 53-62. [pubmed] ↑
8. Rothman AJ, Sheeran P, Wood W. 2009. Reflective and Automatic Processes in the Initiation and Maintenance of Dietary Change, Annals of Behavioral Medicine 38(S1): S4-S17. [pdf] ↑
9. Verplanken B. 2010. Beyond Frequency: Habit as Mental Construct,
   British Journal of Social Psychology 45(3): 639-656. ↑

Are the lessons we have learned up to or appropriate for the challenge of motivating or promoting one-time, infrequent, or rare behaviors? Is a focus on reflection, regular monitoring, and objective feedback going to teach us lessons that help us make the best use (or non-use [1]) of technology to promote behaviors such as health screenings or immunization? Indeed, with affordances such as ubiquitous, context-aware objective monitoring and the ability to deliver rich, tailored feedback at the right time and place, mobile computing may much more to offer for everyday behavior change and maintenance.

The answer may be mixed; many of the lessons and affordances may apply. Mobile and context aware systems can still help us deliver tailored messaging, at the right time and right place (kairos) [2]. Various forms of monitoring may identify people who would most benefit from a screening or from a vaccination. Knowledge of social networks and social messaging can help messages carry greater weight with the recipients.

But these problems may present unique challenges for which we, as a research and professional community, have developed less expertise. What are the right engagement points for one-time messaging, when people are not installing applications and interacting with them on a day-to-day basis?

Just as the public health community prefers some health behavior change models for day-day behavior change (e.g., Theory of Reasoned Action [3] & Theory of Planned Behavior [4]) and others for screening or other infrequent behaviors (e.g., the Health Belief Model [5]), the pervasive heath and persuasive technology communities may benefit from developing a different set of guidelines and best practices for this different category of behaviors.

This difference is recognized in models and frameworks such as the Fogg Behavior Grid, which recognizes trying to do a new or familiar behavior one time as a behavior change challenges. The recommended strategies, however, seem represent assumptions that all behavior change of this type is hindered by the same set of barriers. For one-time, new behaviors (“green dot behaviors“), the guide argues:

the main challenge that we face while triggering a Green Dot behavior is a lack of ability. Since Dot behaviors occur only once, the subject must have enough knowledge to successfully complete the action on the first attempt. Otherwise, frustration, and quitting, may occur.”

before moving on to note that motivation and triggers also matter. And for one time, familiar behaviors (“blue dot behaviors“), the recommendation is:

Blue Dot Behaviors are among the easiest to achieve. That’s because the person, by definition, is already familiar with the behavior. They know how to perform it (such as exercise, plant a tree, buy a book). In addition, they already have a sense of the costs and benefits for the behavior… With Blue Dot Behaviors, people do not require reassurance (enhancing motivation) or step-by-step instructions (increasing ability). Instead, the challenge is on timing: One must find a way to deliver a Trigger at a moment when the person is already Motivated and Able. This timing issue is well known: ‘Timing is everything.’

These recommendations and guidelines strike me as overly simplistic. It seems incorrect to assume that someone exercise necessarily sees it as beneficial or is able to exercise properly. Someone might be very able to start a new behavior – a doctor might be recommending a brief screening that is fully covered by an individual’s insurance, but if the individual feels there may be discomfort associated or not understand or believe in the benefits, he or she may still opt out. If these suggestions accurately represent the sum of what we know about persuasive technology for getting people to do one-time behaviors, we have considerably more work to do.

Consider, for example, the challenge of adult immunization. Timing certainly is a barrier, as might be some aspects of ability (having adequate medical insurance or finances to cover it, or knowledge of how to get it for free, for example). But at least some studies find that these are not the most common barriers, with common barriers including misconceptions about vaccines’ costs and benefits — including the belief that because they are healthy, vaccination is unnecessary, or that vaccination has common and negative side effects [6]. Even if the person has received vaccinations before, they may have misconceptions that leave them unmotivated. Or they may have once been able but had their circumstances change — such as by losing access to insurance or a shift in their social network to one that disapproves of vaccinations, or a doctor that is less inclined to remind patients about them.

A framework, then, that errantly, or over-generally, assumes and emphasizes certain barriers and not others may miss more effective opportunities for intervention, interventions that only work with people for whom it has accurately described the barriers. For the vaccination challenge, focusing on changing social norms, and making pro-vaccination norms visible, may be more effective in some communities.

There are also questions about how to deliver technical interventions for one-off activities (or if/when technical interventions are even well-suited). When the challenge is a trigger, getting a patient to install a reminder application that will trigger at an appropriate time (when the seasonal flu shot is available, for example) and context (when in a pharmacy that accepts their insurance). Even then, an individual might not see the benefits to keeping a single-purpose application around and delete it, or may witch phones, in the meantime, making the reminder less effective. Would bundling many one-time behavioral interventions into a single application, perhaps with day-to-day interventions as well, work? For vaccinations, an application to manage a patient’s interactions with a caregiver (including scheduling, billing, suggested vaccinations and screenings, access to health records, and so on), might be optimal. Text4Baby bundles many one-time health tips into a stream of health advice that is timed with expectant and new mothers’ needs; are there other such opportunities?

Furthermore, for health conditions that are more stigmatizing, some traditional techniques to increase motivation may be problematic. Despite the effectiveness of seeing celebrities or friends pursing a health behavior (e.g., the “Katie Couric effect” for colonoscopies [7]), social messages about who in your network has received a screening or vaccine may sometimes disclose more than is appropriate. I applaud efforts, such as Hansen and Johnson’s work on “veiled viral marketing” [8], to develop social triggers that work but are also appropriate for sensitive health behaviors. In their test of veiled viral marketing, individuals could send a social message to someone in their network recommending that they learn about the HPV vaccine – and the recipient would learn that a friend recommended this content, but not which friend, thus preserving the saliency of a social message while still affording some privacy to the sender.

Social proof, on Facebook, that others, including your friends, voted.

It is also possible that some techniques will be better suited for one-time use than ongoing, day-to-day use. Social comparison data has been shown to be effective in yielding higher contributions to public radio [9], reducing energy use (particularly when combined with injunctive norms [10]), and increasing ratings in an online movie community [11]. I would speculate, though, that in at least some long-term, discretionary use applications, some individuals would prefer to avoid sites that regularly present them with aversive comparisons.

From a paper for the Wellness Interventions and HCI workshop at Pervasive Health 2012.

1. Baumer EPS & Silberman MS. 2011. When the Implication is Not to Design (Technology), Proceedings of CHI 2011. [pdf] ↑
2. Fogg BJ. 2002. Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann. [Amazon] ↑
3. Fishbein M. and Ajzen I. 1975. Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. [online] ↑
4. Ajzen I. 1991. The theory of planned behavior, Organizational Behavior and Human Decision Processes 50(2): 179–211. [pdf] ↑
5. Rosenstock IM. 1966. Why people use health services, Milbank Memorial Fund Quarterly 44 (3): 94–127. [pdf] ↑
6. Johnson DR, Nichol KL, Lipczynski K. 2008. Barriers to Adult Immunization, American Journal of Medicine 121(7B): 528-535. [pdf] ↑
7. Cram P, Fenrick AM, Inadomi J, Cowen ME, Vijan S. 2003. The impact of a celebrity promotional campaign on the use of colon cancer screening: the Katie Couric effect, Archives of Internal Medicine 163(13):1601-5. [pubmed] ↑
8. Hansen D and Johnson C. 2012. Veiled Viral Marketing: Disseminating Information on Stigmatized Illnesses via Social Networking Sites, Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. [slideshare, acm] ↑
9. Sheng J, Croson R. 2009. A Field Experiment in Charitable Contribution: The Impact of Social Information on the Voluntary Provision of Public Goods, The Economic Journal 119(540): 1422-1439. [pdf] ↑
10. Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V. 2007. The Constructive, Destructive, and Reconstructive Power of Social Norms, Psychological Science 18(5): 429-434. [pdf] ↑
11. Chen Y, Harper F Maxwell, Konstan J, Li, Sheri Xin. 2010. Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens. American Economic Review 100(4): 1358-98. [pdf] ↑

As a community, or perhaps more accurately, as communities, health and persuasive technology researchers have made considerable progress on understanding the opportunities, challenges, and some best practices for designing technology to support health and wellness. There is an incredibly rich stream of current and past research, as well as commercially available applications to support a variety of health behaviors.

I think that there remain some under-researched challenges, and I question whether our existing knowledge and research directions can sufficiently address these challenges. If not, what else we should be including in our research discussions and plans. In particular, are doing enough to study one-time interventions and the process for tapering, weaning, or graduating people off of the interventions we build and deploy?

Over the next few days, I’ll be posting my thoughts on the challenges of one-time use and designing for tapered use. I’d love your feedback and your thoughts on other areas that are under-explored or studied in the persuasive health system communities.

You can access it as a standalone app or on Facebook. I’m just doing friends and family testing and some refinements on it for now, so feedback is welcome.

Alphabetical order.

• Adele, 21
• Beirut, The Rip Tide
• Blind Pilot, We Are the Tide
• Bon Iver, Bon Iver
• The Black Keys, El Camino (sadly, it may have come out too late to make some of the early top 10 lists for the year, and was severely underrated by Pitchfork)
• Cut Copy, Zonoscope
• The Decemberists, The King is Dead
• Florence + the Machine, Ceremonials
• The Mountain Goats, All Eternals Deck
• PJ Harvey, Let England Shake
• TV on the Radio, Nine Types of Light

Others that really deserve a mention: St. Vincent, Strange Mercy; Foster the People, Torches; Tennis, Cape Dory; Feist, Metals; Laura Marling, A Creature I Don’t Know; M83, Hurry Up, We’re Dreaming; Wye Oak, Civilian; Head and the Heart, Head and the Heart (really a 2010 release, but remastered and re-released on Sub Pop this year); Drake, Take Care; Destroyer, Kaputt.

Top 11 Movies

This has been a harder year for movies, with many of the best coming at the end.

• Contagion
• Drive
• Margin Call
• Mission Impossible Judge if you will. This movie, perhaps more than any of the other MI movies, knew what it was, embraced it, and was slickly made – making it a lot of fun. When it was ridiculous, I laughed with, rather than laughing at or cringing. Solid brain candy movie.
• Moneyball
• The Girl with the Dragon Tattoo
• Tinker, Tailor, Soldier, Spy
• 50-50

Honorable mentions: The Guard, Ides of March (very well made, but it doesn’t make the cut as it was rather depressing without adding anything), Cave of Forgotten Dreams, Midnight in Paris, The Adjustment Bureau (basically a running movie, not sure why I liked it as much as I did)

Movies I still want to see: Tintin, The Descendants.

11 other highlights

For reading this year, I really enjoyed Sherry Turkle’s Alone Together and Brian Christian’s The Most Human Human, and am looking forward to Kahneman’s Thinking Fast and Slow. I was super-happy to start reading the Song of Ice and Fire series, and immensely enjoyed HBO’s Game of Thrones adaptation.

This year’s season of Breaking Bad was exceptionally good even for an overall exceptional series, as was the second season of Justified. Parks and Recreation continues to be perhaps the sweetest, funniest, consistent comedy on television. House continues to stay fresh, somehow. The BBC modern re-imagining of Sherlock is even better than House. Prime Suspect is the latest darker police procedural that I enjoy but will inevitably be canceled.

At the end of October, he critiqued Kim Brooks’s qualms about defriending people who post things like “Just turned off the t.v. More lies from B. Hussein Obama.” This sort of unfriending happens quite a bit on Facebook and Twitter (Sibona and Walczak 2010; Kwak, Chun, Moon 2011), but Brooks was wondering if she was creating a liberal echo chamber for herself, as she chooses to “simply opt out of such challenges, to crop the frame in whatever way suits our political orientation or cultural sensibility.” If many people come to rely on social streams to point them at their news — oh hello Facebook social reader (still one of their creepiest features as I experience it), hello Twitter — this sort of homophily would take Sunstein’s fears of a DailyMe and selective exposure and raises them to yet another level.

Pierce, however, says, no, this is a perfectly reasonable action. He argues that

…they’re low-information assholes, most of them, and they and their ilk have done untold damage to this country. It is alright to de-friend these people. It is alright never to have anything to do with them again. It is alright to believe that most of their ideas are half-baked, and the ones that aren’t are utterly vile, and all that you would ever learn from them is how to be as nasty a human being as you can be.

Like, I said, bombastic. And polarizing. Most of the comments are in agreement with Pierce’s point of view. The cynic in me thinks he might be right; posts like the one Brooks mentions really don’t have any bearing on my political opinions, other than to possibly reinforce them, though more thoughtful critiques might. I also really do believe that there is a point where others’ opinions get so vile, and so hardened, that one really ought to cut them out and disengage.The optimist in me, though, wants to say: no, this is the chance to engage, presumably you have some sort of other relationship to protect. This is what motivated my study of political discussion in non-political blogs. (& then the cynic in me responds, at which point, this will probably make *them* defriend *you*).

This leads to an issue I’d like to explore in my own work, or have others beat me to: when confronted with disagreeable political opinions in social media streams, (a) when can (and can’t) one engage to possibly affect opinion change, or at least reconsideration, and (b) how can one most effectively do so? From what we learn from that, are there ways to shape the Facebook feed or other streams to show people diverse views from friends that they can engage with, rather than that repulse them? I’ve worked a good deal on related questions in news aggregators, but as a lot of the action moves to Twitter and Facebook, these spaces, and understanding the ways to present and engage with a variety of opinions in them, become more important.

For each paper, the authors note the theories (if any) that were cited as guiding the development of the intervention. Most common were Theory of Planned Behavior, the Health Belief Model, Self-Determination Theory, Social Cognitive Theory, Self-Regulation Theory, the Transtheoretical Model, and cognitive-behavioral theory. The authors find that these theories are somewhat lacking for their ability to to guide the use of the affordances of mobile technology. In particular, theories based on linear progression tend not to take full advantage of mobile devices’ abilities for context awareness (through sensing and prompted input), increased variation in timing and content of interventions, and real-time awareness of outcomes.

While the health behavior theories may not be up to the task on their own, there are theories from a variety of disciplines that can guide, and are guiding, intervention designers. The authors suggest looking to control theory to address some of the shortcomings they identify, and offer a couple of interesting examples. The HCI and social computing community, which knows interactive systems and technology mediated social interaction, also has quite a bit to offer. Though HCI often borrows or adds to theories from other disciplines — including from the health community — it has developed quite a bit of expertise about how to implement these in systems, and developed its own applicable frameworks, such as the Reader to Leader Framework.

The way the authors found the literature they reviewed — a search of Medline — highlights a bit of a communication gap between these disciplines. No papers from HCI conferences or journals were included in this review, despite work such as UbiFit, MAHI, and Fish’n'Steps being excellent examples of the type of studies the authors reviewed. The authors are not alone in excluding HCI papers from review articles on this topic (e.g., [1], [2]). I agree, to a large extent, with Klasnja et al that deep understanding of users’ experiences with behavior change strategies, as implemented in a system, is one of the primary contributions HCI make can to this health behavior change work. Stopping at such findings, however, may be insufficient for the results to have an impact outside of the HCI community, much as eco-feedback work in HCI has not always reached beyond the HCI community.

Collaborations between the HCI and health communities, such as UCSD’s Center for Wireless & Population Health Systems, the iMed group at UW, or Northwestern’s newly announced Center for Behavioral Intervention Technologies, as well as boundary spanning venues such as WISH, Health 2.0, or the Designing for Healthy Living special issue of IJMI, are helping to close some of the gap between communities. We should, however, keep asking whether this will be enough.

We should also keep asking what might be on a research agenda that combines the strengths of HCI and health behavior change communities (or at least, I should, since I have a dissertation to write), with an eye toward advancing both theory and practice. Some thoughts:

• What are appropriate ways to elicit social support or social pressure from others on social network sites or other technology mediated spaces?
• When does anonymity support people trying to make healthy behavior changes, such as by helping them feel comfortable sharing, and when it is a barrier to making those changes, such as by reducing accountability?
• Are some types of nudges that work once or in non-discretionary use problematic over the long term, such as because people start avoiding the system and aversive feedback?
• How can we most effectively use leaderboards to support behavior change and maintenance? With data from many people, there are many more options for how to construct leaderboards than in smaller groups. For a given behavior or set of people, which comparison direction(s) and dimensions of similarity are most effective?
• How applicable are health behavior change theories to other behavior change domains, such as eco-feedback? Where do the existing theories apply, where do they break? We already see things like the health belief model being applied to computer security practices.
• As features used to nudge or support decision making become more widely adopted, do some feature remain effective or become like infrastructure while others become less effective?
• What are the privacy implications of integrating sensing, storage, and sharing of of data? Is HIPAA policy even up to this task?
• When do behavior nudges become coercive? What are good practices for ethics of these interventions? While many counseling strategies promote autonomy and intrinsic motivation as both a matter of efficacy and ethics, does this balance shift as devices that allow constant and long term monitoring (and thus control) become available? For example, some people can already save money on their insurance by agreeing to wear a pedometer and meeting certain step goals.

These are by no means completely open questions. Researchers and practitioners in both communities have been tackling many of them and making progress. It is also by no means a complete list.