Behaviour Change & Technology

Before determining if an Internet-delivered intervention has been disseminated successfully, one should first determine when an intervention is successfully disseminated. This is not as straightforward as it may seem. Absolute figures may cause unrealistic optimism, since thousands of visitors are not uncommon for websites in general. Relative figures, on the other hand, may cause unrealistic pessimism, since they may be very low if one relates number of visitors to Internet penetration rates. To estimate the reach of a recruitment approach and the generalisability of results, it is important to report the target group, the number exposed to recruitment, the number who respond, the number eligible, and the number who actually participate (Graham, Bock, Cobb, Niaura, & Abrams, 2006). Furthermore, one should determine a final target in advance. There is, however, no “default” cut off point for such a target to determine successfulness of dissemination, since this may differ per intervention (e.g., an intervention aimed at reducing alcohol use among high school students versus party drug use among school drop-outs). Issues such as the general accessibility of the target group and the prevalence of behaviours among the target group should be taken into account when determining a final target for successfulness of dissemination.

The determination if an Internet-delivered intervention has been disseminated successfully does not only depend on your final target, but also on the denominator you choose to estimate reach. In a study by Graham et al. (2006), for example, Internet users were recruited based on use of the terms quit(ting) smoking or stop(ping) smoking in a major search engine. When a user clicked on a link to the intervention in the results of a search engine query, an intercept page appeared inviting them to participate in the study of Graham and colleagues. If they accepted, three questions were asked to determine preliminary eligibility. Reach estimates vary depending on the denominator selected: 2.7% of all Internet users seeking cessation information; 6.9% of those who demonstrated preliminary interest in the study; 13.7% of those who were eligible to participate; 21.1% of those eligible and recruited; and 51.3% of those consented.

This also raises an interesting statistical issue; since it is difficult to identify the population to which samples refer when there is no clear sampling method (i.e., everybody can access your intervention). Questions need to be asked to determine eligibility to participate. It remains unclear, however, whether missing data regarding eligibility is due to visitors’ perception of non-eligibility (e.g., parents visiting the intervention because they are interested in the subject or non-smokers accidentally hitting on a website aimed at smoking cessation) or actual drop-out of potential participants. Appropriate methods of analysis are needed to deal with the vast amount of missing data (Christensen, Griffiths, & Korten, 2002) and to determine the recruited sample size from your target group.

The difficulties regarding measurement of online reach are not limited to the field of health promotion. The Interactive Advertising Bureau Europe (IAB Europe) and the European Interactive Advertising Association (EIAA) have announced to develop a measurement standard for website reach. In this project, denominated as Measurement of Interactive Audience Project (MIA Project), several online measurement methods will be evaluated (Marketing Online, 2007).

Implications for practice
It is recommended to report the target group of your intervention, the number exposed to recruitment, the number who responded, the number eligible, and the number who actually participated to determine whether dissemination has been successful. These numbers should be compared with a final target which should be determined in advance and which depends on the intervention (e.g., its subject and target group). Despite this dependence on the intervention itself, one may look at other Internet-delivered interventions to get an idea about the order of magnitude of a suitable final target.

Implications for future research
Analyses regarding data from Internet-delivered interventions are not as straightforward as they may seem, since data can be missing for several reasons (e.g., visitors which do not perceive the intervention as being relevant to them once they logged on or visitors whose needs are fulfilled halfway the intervention). Although it is probably too conservative to consider all missing data as drop-outs, this is recommended as long as appropriate methods of analysis are lacking. Furthermore, due to the openness of Internet as a medium, not all visitors of your intervention are necessary members of your target group (e.g., parents, people who are generally interested in the topic of your intervention). These “participants” should be excluded from data analysis.

References:
Christensen, H., Griffiths, K. M., & Korten, A. (2002). Web-based cognitive behavior therapy: analysis of site usage and changes in depression and anxiety scores. Journal of Medical Internet Research, 4, e3.

Graham, A. L., Bock, B. C., Cobb, N. K., Niaura, R., & Abrams, D. B. (2006). Characteristics of smokers reached and recruited to an internet smoking cessation trial: a case of denominators. Nicotine & Tobacco Research, 8, S43-48.

Marketing Online. (2007). Europese standaard voor meten online bereik in de maak [European standard for measuring online reach in preparation]. Retrieved October 26, 2009, from http://www.marketingonline.nl/nieuws/ModuleItem52230.html.

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In Susan Weinschenk’s (2009) book ”Neuro Web Design” she writes about how websites influence people’s choices unconsciously. Weinschenk shows how influence and persuasion is wired into our brains. This essentially explains why we find attractive people or dangerous car chases persuasive without even being aware of that. Many of the examples used center around the known principles from Cialdini’s (2007) seminal book “Influence”. The best is of course to read both Weinschenk’s and Cialdini’s books, however, if you have little time available, here is a summary of some of the main principles of persuasion:

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1. Active participation creates involvement. All social network sites require active participation by its members and they are also the fastest growing websites. I have no idea what the next big thing in technology is going to be, but the safest bet is on technology that is used to communicate and be social. There is nothing people enjoy more than uploading personal photos, sharing videos, expressing their opinions, etc. Someone once said a bit humorously: “Anyone who denies that they enjoy talking about themselves is either lying or lacking in self-understanding”. I would not agree with the statement literally, but I agree that we love to share our selves with others.

2. Low-ball tactics. If you would like people to share some information so you can learn more about the users of your shareware application, you may find it difficult to ask people to submit information online. But, the chances are that if you start by requesting a small donation to keep continuing to make the application, and then request for information, more people will be willing to share information. Being asked to share information when you have declined to donate money may seem like a minor thing to do. However, make sure the initial request seems reasonable and somewhat in proportion to your second request (the target). It would not be a good idea to begin by requesting a donation of €50 and then asking for information.

3. Foot-in-the-door tactic. This is more or less the opposite of low-ball tactics. You begin by making small requests and then increase the scope of your requests. A funny example of the principle in practice was illustrated on a Norwegian television show where an interviewer knocked on people’s doors and simply asked them about how they were living, next he asked for permission to come in and see how they were living, and so it went that he even got to look into people’s bedside drawer a place you would normally not let many people in.

4. Social validation. Learning to know what others think is important. Sometimes we are introduced to products or services we do not know much about. How to decide if this is interesting or good for you? Well, have a look at what others have done or think about it. iTunes’ Genius function is a good example of social proof. Genius recommends music albums and songs based on what music you have on your iPod or iPhone. And you can also see what other people that listen to the same music as you have bought. You can give ratings, write reviews, and read what others think. This can be quite effective, especially when an album or song is just a mouse click away.

5. Reciprocity. Giving someone a preview, demo or sample of your product will make that person feel indebted to you, and no one likes to feel indebted. How do most people resolve this uncomfortable situation? By giving back (or reciprocating). Notice that there is an asymmetrical power relation here too. The person who first performs a service also has the power to decide when he or she wants something in return (“I gave you a demo which ends right here. Now, if you want more, you will have to pay”).

6. Rewards. Rewarding people’s behaviour reinforces that particular behaviour. Simple learning principles like this can be very effective. However, they should be used with caution. For example, rewarding people on a variable-ratio schedule makes the rewards seem scarce and unpredictable, and ask anyone with a gambling or gaming problem and they will tell you that a game with a variable-ratio schedule is more addictive than any other game. Rewards can also switch the motivation for using a technology from intrinsically to extrinsically driven and we know that people often lose interest in what they were doing when this happens. Not sure how this motivational change can affect business? Here is a little thought experiment: When you start paying a child that enjoys making puzzles a small amount of money (e.g. €1) for each puzzle they complete for a short period of time. And then you withdraw the reward (money). If you compare the number of completed puzzles, do you think the child would complete more or fewer puzzles after the inducements compared to before?

We have covered six of the principles here and now. There are more, so stay tuned!

References

Cialdini, R. B. (2007). Influence: The psychology of persuasion. HarperCollins: New York.

Weinschenk, S. M. (2009). Neuro web design: What makes them click? New Riders: Berkeley, CA.

See also

www.whatmakesthemclick.blogspot.com

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The digital environment (e.g. Internet, mobile phones, smart phones) that is now an integral part of our daily lives is becoming an increasingly important means of sustaining the health of people worldwide, whether by providing access to a wealth of information, by linking geographically dispersed communities of peers and professionals, or by supporting self-management of health and illness. The Internet is therefore rapidly becoming both a medium and a focus for health psychology research.

The Internet can be used as a source of naturally occurring observations and data, as in the qualitative study by Rodham, McCabe, and Blake (2009) of Internet communication between people with Complex Regional Pain Syndrome. It can also be used to give people personalized feedback about their health risks, as in the study of predictors of online diabetes risk test taking by van Koningsbruggen and Das (2009). Perhaps most significantly, it provides a cost-effective means of making automated behavior change interventions widely available, such as the stress and alcohol reduction programmes deployed in two papers in a recent issue of Psychology & Health (Crutzen et al., 2009; Fridici, Lohaus, & Glaß, 2009).

A meta-analysis of 75 randomized controlled trials has provided support for the effectiveness of digital interventions (Internet, mobile phones, etc.) in the health promotion area (Portnoy, Scott-Sheldon, Johnson & Carey, 2008). But how and why can digital interventions be effective in promoting sustained behaviour change? And can different digital tools serve different purposes?

In Eysenbach’s (2001) definition of the ‘10 E’s’ integral to e-health, three of the key ingredients are encouragement of a new relationship with the patient which focuses on empowering and educating them. Digital interventions can provide new forms of access to self-care, video and audio delivery for those with reading difficulties and anonymous social support for those who are unable or unwilling to consult health professionals in person. While there is concern that the ‘digital divide’ could limit the extent to which those from more socioeconomically deprived backgrounds may benefit from digital interventions (Murray, Burns, See, Lai, & Nazareth, 2005), this problem may diminish as the Internet grows more ubiquitous; for example, young people already routinely use the Internet daily, with minimal differences in access due to socioeconomic background.

Another key problem is the finding that high attrition rates seem to pose a potential short-coming of digital health interventions (see for example Matano, Koopman, Wanat, Winzelberg, Whitsell, Westrup, Futa, Clayton, Mussman & Taylor, 2007). Clearly, we need research into how digital interventions can best be designed to hold the interest of the user. While the main motivation for initial use is the expected utility of use, continued use is probably heavily influenced by experienced utility (which in turn fuels expected future utility). This may be increased by offering the client relevant, individually tailored material and feedback, which has been shown to increase program use and engagement (see e.g. Strecher, McClure, Alexander, Chakraborty, Nair, Konkel, Greene, Couper, Carlier, Wiese, Little, Pomerleau & Pomerleau, 2008). Specifically, individuals must be able to gauge their progress against some frame of reference, which might include their own change plan (compared with their own prior history), the behavioral progress of others who are in a similar situation to themselves, or a regimen from a trustworthy source (for overview see Kraft, Drozd & Olsen, 2009). In other words, efforts to change are likely to be successful when individuals receive timely monitoring and feedback on their progress (see e.g. Brendryen, Drozd & Kraft, 2008). With such feedback, individuals can be motivated by their own achievements. They can modify their strategies and gauge the proximity of their goals. For example, Internet weight loss programs are likely to be most effective when they require participants to keep regular records of their food intake and physical activity, and the program provides feedback on their performance (Tate, Jackovny & Wing, 2003).

Additionally, more elaborate interventions can be more effective. For example, a review of 15 Internet-delivered interventions to promote increased physical activity (Vandelanotte, Spathoris, Eakin, & Owen, 2007) found that interventions were more successful if they required participants to engage more with the intervention (using techniques such as email, weekly modules, or online coaching or chat sessions). Generally, increased interactivity seems to improve the emotional quality of an intervention (i.e. how it makes you feel) (Norman, 2003). Hence, the extent that interactivity can increase user involvement must be considered a critical characteristic of a digital intervention.

It is pretty obvious that we are just at the start of the development of digital health care. Rapid technological change means that this field is already posing new questions and challenges for digital interventions and research. For example, many future interventions can be expected to be delivered through mobile phones, which will offer new opportunities for tracking people’s activities and health status and offering timely advice. At the same time, a more focused and tailored approach to intervention design may be needed that is compatible with the restrictions of interacting via a smaller interface. Given the exciting scope for innovation and the demand for technological solutions to health problems, it is certain that research into digital interventions is set to grow substantially in the foreseeable future.

References

Brendryen, H., Drozd, F. & Kraft, P. (2008). A digital smoking cessation program delivered through Internet and cell phone without nicotine replacement (Happy Ending): Randomized controlled trial. Journal of Medical Internet Research, 10, e51.

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): new methods for more potent eHealth Interventions. American Journal of Preventive Medicine, 32, S112-S118.

Crutzen, R., de Nooijer, D., Brouwer, W., Oenema, A., Brug, J., & de Vries, N. (2009). Effectiveness of online word of mouth on exposure to an Internet-delivered intervention. Psychology and Health, 24, 651-661.

Eysenbach, G. (2002). Issues in evaluating health websites in an Internet-based randomized controlled trial. Journal of Medical Internet Research, 4, e17.

Fridici, M., Lohaus, A., & Glaß, C. (2009). Effects of incentives in web-based prevention for adolescents: Results of an exploratory field study. Psychology and Health, 24, 663-675.

Kraft, P., Drozd, F. & Olsen, E. (2009). ePsychology: Designing theory-based health promotion interventions. CAIS, 24, 399-426.,

Matano, R., Koopman, C., Wanat, S., Winzelberg, A., Whitsell, S., Westrup, D., Futa, K., Clayton, J. Mussman, L. & Taylor, C. (2007). A pilot study of an interactive web site in the workplace for reducing alcohol consumption. Journal of Substance Abuse Treatment, 1, 71-80.

Murray, E., Burns, J., See, S. T., Lai, R., & Nazareth, I. (2005). Interactive health communication applications for people with chronic disease. Cochrane Database of Systematic Reviews, Issue 4, Art. No.: CD004274.

Norman, D. A. (2003). Emotional design: Why we love (or hate) everyday things. New York: Basic Books.

Portnoy, D. B., Scott-Sheldon, L. A. J., Johnson, B. T. & Carey, M. P. (2008). Computer-delivered interventions for health promotion and behavioural risk reduction: A meta-analysis of 75 randomized controlled trials, 1988 - 2007. Preventive Medicine, 47, 3-16.

Rodham, K., McCabe, C., & Blake, D. (2009). Seeking support: an interpretative phenomenological analysis of an Internet message board for people with Complex Regional Pain Syndrome. Psychology and Health, 24, 619-634.

Strecher VJ, McClure JB, Alexander GL, Chakraborty B, Nair VN, Konkel JM, Greene SM, Collins LM, Carlier CC, Wiese CJ, Little RJ, Pomerleau CS, Pomerleau OF. (2008). Web-based smoking-cessation programs: results of a randomized trial. American Journal of Preventive Medicine, 5, 373-81.

Tate, D. F., Jackovny, E. H., & Wing, R. R. (2003). Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes. Journal of the American Medical Association, 289, 1833-1836.

van Koningsbruggen, G. M. & Das, E. (2009). Don’t derogate this message! Self-affirmation promotes online type 2 diabetes risk test taking. Psychology and Health, 24, 635-649.

Vandelanotte, C., Spathoris, K. M., Eakin, E. G., & Owen, N. (2007). Website-delivered physical activity interventions. American Journal of Preventive Medicine, 33, 54-64.

A full version of this article can be found here:

Kraft, P. & Yardley, L. (2009). Current issues and new directions in psychology and health: What is the future of digital interventions for health behaviour change? Psychology & Health, 24, 615–618.

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Evidence from efficacy trials indicates that exposure rates to Internet-delivered interventions are low (De Nooijer, Oenema, Kloek, Brug, De Vries, & De Vries, 2005), and they may be even lower when these interventions are implemented in real life rather than in a research setting (Evers, Cummins, Prochaska, & Prochaska, 2005). Exposure of individuals to the intervention content, through use of the intervention, is necessary since attention is a prerequisite to establish desired behaviour change (McGuire, 1985). Therefore, it remains important to assess exposure to Internet-delivered interventions.

There are several measures to assess exposure to Internet-delivered interventions, such as frequency and duration of visits, but there is no gold standard. Each exposure measure relates to a different aspect of exposure (Danaher, Boles, Akers, Gordon, & Severson, 2006). One can visit an intervention very frequent, for example, but only for a short period of time. Duration of visits, on the other hand, does not necessarily give a clear picture of participants’ online activity, since one does not know to how much information participants are possibly exposed. Therefore, number of visited web pages would be more appropriate to assess online activity. All such exposure measures can be tracked objectively and are, in contrast to self-reported exposure measures, independent of participants’ memory, interpretation, or social desirability.

Linking exposure measures to variables at the individual level

To fruitfully utilise exposure measures it has to be possible to link them to variables at the individual level, to be able to compare subgroups that differ on socio-demographic, psycho-social, or behavioural measures. Moreover, exposure measures on the individual level can also be linked to outcome measures of an intervention. By doing so, it is possible to study potentially mediating effects of objectively tracked exposure on interventions’ outcome measures. The latter, however, is no common practice to date (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, submitted). For example, recent studies (An et al., 2006; Chen & Yeh, 2006; Escoffery, McCormick, & Bateman, 2004) reported a limited number of exposure measures and did not relate them to the intervention’s outcome measures, making it impossible to study potentially mediating effects of exposure.

Practical implications

There are no known technical barriers to track exposure measures of Internet-delivered interventions. It is important, however, to realize this from the start of an intervention development process and to involve technical staff during this initial phase (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, e-pub ahead of print). Furthermore, dependent on the laws in certain countries or states, it may be necessary to take additional steps. In a recent Dutch project, for example, it was necessary to register the project at the Dutch Data Protection Authority, which supervises the fair and lawful use and security of personal data (Crutzen, De Nooijer, Candel, & De Vries, 2008). If these legal issues are covered, we recommend to track as many exposure measures as possible since there is no gold standard. Furthermore, having exposure measures available is also useful during process evaluation of Internet-delivered interventions, as has been shown in other studies (Barak & Fisher, 2003; Lou, Zhao, Gao, & Shah, 2006; Patten et al., 2007; Roberto, Zimmerman, Carlyle, & Abner, 2007). These exposure measures provide detailed insight into where participants either leave the intervention website or have come to a standstill. This information can be used to adapt interventions to users’ needs and therewith increase exposure rates and probability of behaviour change.

References:

An, L. C., Perry, C. L., Lein, E. B., Klatt, C., Farley, D. M., Bliss, R. L., et al. (2006). Strategies for increasing adherence to an online smoking cessation intervention for college students. Nicotine & Tobacco Research, 8 (S1), S7-S12.

Barak, A., & Fisher, W. A. (2003). Experience with an Internet-based, theoretically grounded educational resource for the promotion of sexual and reproductive health. Sexual and Relationship Therapy, 18, 293-308.

Chen, H.-H., & Yeh, M.-L. (2006). Developing and evaluating a smoking cessation program combined with an Internet-assisted instruction program for adolescents with smoking. Patient Education and Counseling, 61, 411-418.

Crutzen, R., De Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & De Vries, N. K. (e-pub ahead of print). A conceptual framework for understanding and improving adolescents’ exposure to Internet-delivered interventions. Health Promotion International.

Crutzen, R., De Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & De Vries, N. K. (submitted). How to facilitate exposure to Internet-delivered health behavior change interventions aimed at adolescents or young adults? A systematic review.

Crutzen, R., De Nooijer, J., Candel, M. J. J. M., & De Vries, N. K. (2008). Adolescents who intend to change multiple health behaviours choose greater exposure to an Internet-delivered intervention. Journal of Health Psychology, 13, 906-911.

Danaher, B. G., Boles, S. M., Akers, L., Gordon, J. S., & Severson, H. H. (2006). Defining participant exposure measures in web-based health behavior change programs. Journal of Medical Internet Research, 8, e15.

De Nooijer, J., Oenema, A., Kloek, G., Brug, J., De Vries, H., & De Vries, N. K. (2005). Bevordering van Gezond Gedrag via Internet: Nu en in de Toekomst [Promotion of Healthy Behavior through the Internet: Now and in the Future]. Maastricht: Maastricht University.

Escoffery, C., McCormick, L., & Bateman, K. (2004). Development and process evaluation of a web-based smoking cessation program for college smokers: innovative tool for education Patient Education and Counseling, 53, 217-225.

Evers, K. E., Cummins, C. O., Prochaska, J. O., & Prochaska, J. M. (2005). Online health behavior and disease management programs: are we ready for them? Are they ready for us? Journal of Medical Internet Research, 7, e27.

Lou, C. H., Zhao, Q., Gao, E. S., & Shah, I. H. (2006). Can the Internet be used effectively to provide sex education to young people in China? Journal of Adolescent Health, 39, 720-728.

McGuire, W. J. (1985). Attitudes and attitude change. In M. Lindsay & E. Aronson (Eds.), The Handbook of Social Psychology (pp. 233-346). New York: Random House.

Patten, C. A., Rock, E., Meis, T. M., Decker, P. A., Colligan, R. C., Pingree, S., et al. (2007). Frequency and type of use of a home-based, Internet intervention for adolescent smoking cessation. Journal of Adolescent Health, 41, 437-443.

Roberto, A. J., Zimmerman, R. S., Carlyle, K. E., & Abner, E. L. (2007). A computer-based approach to preventing pregnancy, STD, and HIV in rural adolescents. Journal of Health Communication, 12, 53-76.

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Language is a great example of dual information processing. One part of language is explicit and controlled and another part is implicit and automatic. Explicit language processes are consciously manipulated by people such as the choice of words, intended meaning, and intonation, while implicit language processes are largely unconscious and receive little direct attention.

To explain or not to explain?
Looking at the details of language can give us much information about people’s attitudes and inclined behaviours, as people occassionally disclose their attitudes or what they are going to do in a manner of the way they communicate but not in the content. For example, if one expects a Black American male “Darnell” who is a short-distance runner, to be quick on his feet, learning that “Darnell came in sixth place…” may instigate an attempt to explain his disappointing position (”… because he was slightly injured). Attempts to explain behaviour most often occur to make sense of things when there is an incongruity in attitudes or behaviour (American runners are known for being among the best short-distance runners in the world).

However, learning that “Darnell stole an old lady’s purse” is unlikely to be explained. Why? Because the information is congruent. And when information is congruent, it often means that our (stereotypic) expectancies have been confirmed. Thus, just by paying attention to the language, we have come to learn that the fictitious person in our example has a mental representation of Black Americans as both athletic and criminal. But merely having mental representations or articulating these representations in language does not necessarily lead to behavioural consequences.

One example that can lead to behavioural consequences is when subordinates communicate to a female leader that they perceive her as “too soft” or “unprofessional”. Consequently, female leaders sometimes shift their leadership style to a more powerful and masculine style. Language changes to become more directive, task-related, and generally contains few tag questions (e.g. “It could have been better, don’t you think?”), hedges (e.g. “I’m not an expert, but…”), and hesitations (e.g. “…uhm…” or “hmm…”). This sometimes happens when female leaders are put under stereotype threat that they are poor leaders. It is important to note that such reactance is not only reserved for female leaders. It can strike males too, and people in all positions and situations.

Promotion or prevention?
Motivated cognition can definitely translate into behaviour as Spiegel, Grant-Pillow, and Higgins (2004) demonstrated. They assigned participants the goal of writing a report about their leisure time. All participants were given the same set of instructions, except that one half were instructed to focus on taking advantage of appropriate times, places, and methods in writing their reports (i.e. a promotion focus), and the second half were instructed to focus on avoiding bad times, places, and methods in writing reports (i.e. a prevention focus). They were also assessed on regulatory focus prior to the task. Both those who were promotion focused and were given the promotion focused instructions and those who were prevention focused and were given prevention focused instructions were about 50% more likely to mail their reports than those where there was no regulatory fit (i.e. promotion focused/prevention focused instructions and prevention focused/promotion focused instructions). Clearly, paying attention to linguistic details and applying language with intent can dramatically change the “persuasiveness” of arguments.

Conclusion
Implicit use of language can be used beyond the few examples given herein to instigate cognitive, emotional, and behavioural change. It represents a low-budget and low-tech approach to both increasing the persuasiveness of arguments and attitudinal and behavioural measurements. But it also gives us access to information about people that may be inaccessible for different reasons (e.g. because language is largely an unconscious process or because people do not want to disclose sensitive information such as their racial preferences).

Key reading:

Petty, R. E., Fazio, R. H. & Brinol, P. (2009). Attitudes: Insights from the new implicit measures. Psychology Press: New York.

Reference:

Spiegel, S., Grant-Pillow, H. & Higgins, E. T. (2004). How regulatory fit enhances motivational strength during goal pursuit. European Journal of Social Psychology, 34, 39-54.

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When we describe behavior change via technology, we are describing a designer’s intention being argued via technology. The designer wants the user of a system to change his perception about the world and ultimately his behavior. The change is achieved by designing the system in a manner that would persuade the user to actually change. Currently we don’t have a model for analyzing such technology embedded arguments and furthermore it’s hard to see how we might shape such a model.

Arguments persuade - what are arguments in persuasive systems?
Most persuasive systems focus on the relationship between man and machine, where the machine is undertaking the role of persuading the user of a certain behavior. Typically systems are deliberately crafted in a manner that would lead the user to adopt this target behavior, so that they may act as the designer’s vehicle of persuasion.

This closely resembles traditional communication. The ‘designer’ of a speech deliberately crafts it to persuade the audience.  When giving a speech or writing an article, we embed arguments into language itself by formulating and saying the actual arguments that the audience hears. As we look at persuasive systems, it becomes harder to determine where the arguments actually reside and even harder to analyze them.

Embedded arguments: Driving persuasion in software
Redstöm (2006) was the first to point out, that we may perceive persuasive systems as systems where a designer has deliberately embedded an argument into the system itself. As systems naturally have no intention themselves the intention is infused into the system by the designer. His intentions are built into the system itself in the form of embedded arguments. The system in turn acts as a vehicle of persuasion by driving the designer’s intentions to the end user.

An argument models for textual arguments
In writing and speech we have various models for arguments that aim at explaining what an argument really is. Such models at least offer a way to analyze existing arguments or to test them before presenting them to a target audience. One such model is Toulmin’s model (Foss, Foss & Trap, 1985) as seen below:

Figure 1: Example of Toulmin’s model used on an argument for smoking cessation

The general concept of this model is that the ‘warrant’ allows for audience to subscribe to the ‘claim’ of the speaker, by connecting the ‘claim’ and ‘grounds’. We pose a ‘claim’, and then we support it by a ‘warrant’ that would allow the listener to actually agree with us based on the ‘grounds’ we present - in essence resulting in persuasion.

‘Backing’, ‘modality’ and ‘rebuttal’ are optional elements that are often times omitted, they concern the level of certainty that we put behind our claim. Notably a warrant may be both: logos (appeal to reason), ethos (appeal to authority) or pathos (appeal to emotions). Using the model we may even find whole hierarchies of arguments within a text. The example given in figure 1 above might easily be extended. For instance the grounds “Smoking causes cancer” may in itself be seen as a claim in a different argument. This claim has its own ‘warrant’ and ‘grounds’ etc. etc.

It’s unclear how an analysis framework for embedded arguments should be formulated
Today it’s not possible to analyze arguments embedded into software in the same clear manner as it is with texts (figure 1.). While persuasive systems mature we have no models for analyzing the arguments that are embedded in them. No models allow for us to interpret the validity of such arguments. We may naturally use Toulmin’s model for analyzing the textual content that a system presents to its user in the form of: text, video or audio. Persuasive systems however employ a myriad of techniques to persuade for instance: interaction, kairos (~ timing), persistence, tailoring, personalization and reduction to mention a few.

The mix of content and interaction makes it very difficult to clearly analyze, why one embedded argument might be more persuasive than another. Why is one health application more successful in motivating users to be more active runners? What argument did the systems really pose? How could we compare the arguments posed by one application to the arguments posed by another? We simply don’t have models for analyzing those types of embedded arguments.

Conclusion
This short article has explored the notion of technology embedded arguments in persuasive software and that embedding of arguments is an elaborate part of designing persuasive systems. Whereas we do have models for analyzing classical arguments as presented in text or speech (exemplified by Toulmin’s model), there is a lack of analysis models for arguments that are embedded into persuasive systems. The complexity of persuasive systems leads us to assume that an analysis model for embedded arguments would also be more complex. Persuasive systems might contain text, video and audio, thus having the same capabilities as traditional media, but they extend far beyond the textual by employing a myriad of interaction strategies. Currently it’s not possible to analyze this added complexity of technology embedded arguments in the same manner as we could if we were for instance analyzing two texts on the same topic.

Key reading(s):

Redstöm, J. (2006) “Persuasive Design: Fringes and Foundations”, p.112-122 in Lecture Notes in Computer Science, Springer Berlin / Heidelberg, ISBN: 978-3-540-34291-5

Foss, S., K., Foss, K., A., Trapp, R. (1985), “Contemporary Perspectives on Rhetoric”, Waveland Press Inc., Prospect Heights, Illinois, ISSN/ISBN: 0-88133-542-8

See also:
Toulmin, S. (1969). The Uses of Argument, Cambridge, England: Cambridge University Press.

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Persuaded by the Fitness-Club

My fitness-club has understood that to earn money they have to gain new members, but also keep the members they already have active! Members that are not using their membership will sooner or later leave the club, so to keep the members happy and active is of great importance. Most members have joined the club to change their exercise habits in some way. To support the members in planning of their training, the club has a web-site where one can book group-training classes like aerobics and spinning. The most popular classes are often full, so by booking in advance the members are secured a ticket to the classes they prefer. This is the obvious reason why booking is nice to offer. But from a psychological view, or you might say a persuasive view, there is more to this booking- system than you might think. We shall now take a look at how this online booking- system actually utilizes basic persuasive principles; familiarity, commitment and consistency, social proof and scarcity (Cialdini, 1993). In addition the booking functions as a self- regulatory tool by helping you plan and self-monitor your exercises.

First of all, the booking takes place on the clubs homepages, so the member is guided to the booking system trough the regular home-site where all news from the club are presented. In this way the club persuades the members to at least take a glance at their homepage where special offers and news from the club are presented. Hence, the booking makes the members more familiar with their homepages. Familiarity underlies almost all advertising. An idea, person or product becomes more familiar and comfortable, and thus more attractive, to us through sheer repetition.

Second, the booking makes you commit to participate in particular classes. The persuasive principle of commitment and consistency is described as our yearning to be (and to appear) consistent with what we have already performed or stated. Once we make a decision, we will feel pressured from within and from the people with whom we interact to behave consistently with that commitment. Thus, by booking the class on internet the member has psychologically committed himself to exercise. You also know that by booking in advance for the class you have occupied one out of the 30 available places for that particular class. So if you don’t show up, it would actually prevent someone else from training. This knowledge will probably enhance your feeling of commitment to show up and join the class.

Third, you are persuaded by social proof and scarcity. Social proof is a psychological phenomenon that occurs in ambiguous social situations when people are unable to determine the appropriate mode of behavior. Making the assumption that surrounding people possess more knowledge about the situation, they will deem the behavior of others as appropriate or better informed. The scarcity principle on the other hand says that we want what we are afraid we can’t have. The words “Closing Down” and “Last Few To Go” are very powerful, because we value what is rare and exclusive. In the fitness-club booking system you get a notice if a class is full booked, and you can choose to be placed on a waiting-list. Thus you both see that this is a popular class that other people are joining (social proof) and you are also reminded that the tickets to the wanted class is a limited resource (scarcity).

When we are changing a habit like starting exercising, it requires effective self-regulation (see e.g. Baumeister, Heatherton & Tice, 1994). Self-regulation can be divided into three sub-processes: (1) self-observation; (2) self-evaluation; and (3) self-reaction. So to have a visualisation of your exercises makes it very clear to you if you have dropped out of training for a week. The bookings are presented to you in a calendar, which allows you to follow your exercise over time in your own training- calendar. Thus, the calendar workss as a self-monitoring tool facilitating the self regulation of your exercise behaviour. In addition the fitness-club helps you with the monitoring and regulation of your exercise behaviour. They also have access to your planning- calendar and actually pick up the phone and give you a call to check if you need help to get back on track if you have dropped out for more than a month. In this way you have no chance to “forget” about your training.

As you can see, the online booking system which by first glance looks like it is made just to help the fitness-club with administration work, actually turns out to be a system which utilizes both persuasive principles as well as helping you self- regulate to make you exercise more often. This is an excellent example of how a pretty simple online tool can persuade and help people that want to change their exercise habits succeed.

References:

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at self-regulation. San Diego, CA: Academic Press.

Cialdini, R. B. (1993). Influence: The psychology of persuasion. New York:Quill.

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Most people adhere to their goals when they are both motivated and able. This is quite impressive in terms of the complex human social life people engage in and the multiple choices people face every day. But how is it that people who are highly motivated and proven to be capable of for instance studying or dieting suddenly may fail to do so? In this article, we discuss how making choices can impair subsequent self-regulation.

Ego depletion
Self-regulation refers to the ability to override or inhibit thoughts, feelings, impulses, and behaviours. It can be viewed as the active part of the self (i.e. agent), as opposed to the self as something known (i.e. identity) or the self as knower (i.e. knowledge). In other words, self-regulation is crucial for the ability to adhere to personal and social goals, but what happens when people’s self-regulatory capacity breaks down?

Vohs and colleagues (2008) conducted a series of experiments where a choice versus no-choice manipulation was followed by a task that required self-regulation. After for instance making active choices on a website, the researchers measured participants’ persistence on a subsequent self-regulatory task. In several of the experiments, participants were also led to believe that practicing or solving the self-regulatory task would help them on an upcoming task so that participants should have been highly motivated to perform well on the self-regulatory task.

The unanimous findings were that making choices leads to lessened tolerance to negative adverse events, lowered persistence, and more procrastination. In other words, making choices depleted the self of mental resources (i.e. ego depletion) that affected subsequent ability to self-regulate or adhere to goals.

Information architecture design
These findings are very interesting from an information architecture (IA) design perspective (see Danaher, McKay & Seeley, 2005). On the one side, we can depend on users to find the right information at the right time and give them complete freedom, including a range of choices and few restrictions (e.g. design digital interventions as matrices). Both users and designers may find it appealing to have a full set of choices and full self-determination although people often report feeling frustrated and overwhelmed with the intense cognitive demands that accompany large amounts of choices (Huffman & Kahn, 1998). And as Vohs et al. and related research shows (e.g. Baumeister, et al. 1998), providing people with many choices is not very helpful or supportive. In addition, from a therapeutic point of perspective, just providing people with choices would be equivalent to holding a laissez-faire attitude as the designer or therapist sort of lets the sequence of events take its own course and takes on a passive role and seeming lack of interest and involvement in the user (Rogers, 1951).

On the other side, we can guide users through a predetermined sequence and reduce or remove irrelevant information (e.g. tunneled IA design). A good example of these principles applied in action is e-commerce where users typically add items in a basket or shopping cart, proceed to checkout, enter shipping address, billing information, and place their order. This may be appropriate and desirable in e-commerce, but when it comes to health and well-being, such IA designs can easily end up objectifying a person’s thoughts, feelings, and behaviours, become too intellectualistic and didactic, and communicate a basic mistrust and lack of respect for the person and belief in his or her abilities to find solutions to their own problems.

Belief in the right for self-determination
The empirical evidence stand in stark contrast to the thought experiments by existentialist philosophers like Camus and early Sartre who portrait the self as an entity that is constructed by acts of free will. Most people strongly believe in the existentialist thought and would have a hard time accepting anything else because a rejection of the existentialist thought would entail that they reject the belief in their basic (human) right for self-determination. People feel it liberating and tend go to great lengths in protecting their freedom. When this freedom or opportunities for making choices is restricted, people become defensive, exhibit patterns of aggression, reactance, and imagine that they control events which they cannot possibly control (e.g. what other people think of them or the roll of dice at casinos). But let us assume that the empirical evidence and existentialist thought are equally true and neither is false, can we then find a solution which does not necessarily imply a compromise?

Is there a solution?
According to Rogers (1951), the counselor’s aim and role or, in our case, an intervention designer’s role is to perceive the phenomenological field as experienced by the person, wholeheartedly accept the person as he or she is which is already experienced critically by the person’s self as it is, and adopt an internal frame of reference. It means to see the world as the person sees it and put aside any preconceived ideas, preconceptions, and perceptions adopted from an external frame of reference (i.e. the counselor or designers perspective). It also means to move in the direction of greater self-responsibility; self-government, self-regulation, and autonomy. The paradox is not the right for self-determination, but it is the choice(s) itself which is the paradox.

Consequently, the potential solution is to help users make the right choices and be supportive of their decision even if it goes against every form of your personal and intellectual sense and understanding of users’ or clients’ problem. This can be an excruciating exercise for the counselor or designer because as human beings we tend to evaluate, compare, diagnose, guide, persuade, argue, teach, etc. quite automatically. Instead, help users decide the overall and important intervention components that strongly underscores that these decisions are fully self-determined rather than providing an excess of alternatives and options concerning nitty-gritty details which users might fancy, but which end up undermining users’ capacity for self-regulation. People want choices, but as Vohs and colleagues’ research shows, people eventually tire of the endless demands and stresses of making these choices. How liberating are these choices of freedom when they actually impair people’s optimal functioning, health, well-being, and social development?

Key reading(s):

Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M. & Tice, D. M. (2008). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Journal of Personality and Social Psychology, 94, 883-898.

References:

Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252-1265

Danaher, B. G., McKay, H. G. & Seeley, J. R. (2005). The information architecture of behaviour change websites. Journal of Medical Internet Research, 7(2), e12.

Huffman, C., & Kahn, B. E. (1998). Variety for sale: Mass customization or mass confusion. Journal of Retailing, 74, 491-513.

Rogers, C. R. (1951). Client-centered therapy. London: Constable.

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It is very interesting to see how the perception of the information presented in choice items can influence people’s decisions and actions. The arrangement of choice alternatives can pertain to different underlying goals such as for example food enjoyment and concerns about weight which can elicit different psychological and behavioural consequences.

Together or separate?
Fishbach and Zhang (2008) found that it does actually make a difference if conflicting goals are presented alone (e.g. chocolate cake) or as choice alternatives (e.g. chocolate cake or vegetable soup). When participants were presented with conflicting choice items together, the choice items were perceived to complement each other and participants tended to evaluate the tempting items more positively. When people hold a positive attitude towards an item, they start balancing the conflicting goals as if they were complementary goals (e.g. “If I have the vegetable soup for entrée, I can have the chocolate cake as a treat for dessert”).

Fishbach and Zhang (2008) also found that when items were presented separately, they seemed to compete against each other and participants then tended to evaluate the goal item more positively. This is also the case when being presented to temptations only, as temptations seem to automatically activate the desired goal (Fishbach, Friedman & Kruglanski, 2003). What seems to happen is that the conflicting goals start to compete against each other when presented separately and thus the more important goal is highlighted (e.g. eating healthy).

Highlighting and balancing goals
There is an interesting link between highlighting or balancing goals and the way goals are represented or framed as discussed in the article How to increase motivation to goal adherence. When people highlight a goal they feel very committed to their goal and see their achievements as a result of their past actions or in terms of what has been accomplished to date. In other words, they exhibit a high level of intrinsic motivation or the feeling that the decision to adhere to their goal is fully self-determined. In contrast, people who balance conflicting goals see their actions as part of a progress (i.e. remains to be accomplished). Consequently, they start to balance the conflicting goals as if they were complementary (”If I eat pizza today, I can keep my diet tomorrow”; for example, see Fishbach & Dhar, 2005, Study 3). The problem is simply that today’s calorie intake is not re-set tomorrow. These effects suggests that a focus on commitment or progress promotes subsequent choices of action that either highlights the goal or balances between alternative goals.

Implications
Obviously, helping users or clients decide what goals are important, setting unambiguous goals, strengthening commitment towards change, and avoiding presenting or discussing conflicting goals at the same time seem to highlight important personal goals. However, one important consequence from this discussion is perhaps that we should abandon discussing barriers to treatment altogether as discussing barriers most often involves presenting information about conflicting goals at the same time which according to Fishbach and colleagues’ research results in a balancing of goals (”If I pack my gym bag now, I can go watch TV”). But this may be at odds with both treatment providers and users or clients who often consider addressing barriers very important.

Another very interesting implication of the perception of choice items that have underlying conflicting goals comes from priming studies (see e.g. Stroebe, Mensink, Aarts, Schut & Kruglanski, 2008). Priming refers to the phenomenon of activating concepts by exposing people to objects that increases the accessibility of the mental representation of that object or concept. For example, a website concerning nutritional counseling or weight management may present words or pictures relating to the semantic concept of food enjoyment (e.g. words like tasty or appetizing). At the same page, the website may present words or pictures relating to the semantic concept of eating healthy (e.g. words like slim or nutritious). This seems to trigger psychological processes such as balancing between conflicting goals, creating commitment uncertainty, making short-term goals more salient, etc. - processes that are all related to unsuccessful behaviour change. The implication is that one should avoid triggering a balancing of conflicting goals, but rather focus on food enjoyment or eating healthy separately to activate a highlighting.

Conclusion
It appears to be unproblematic to provide users with for example healthy and nutritious and delicious and tasty recipes or food items separately. The problem is when people are presented with both at the same time which unfortunately often is the case. Just imagine all the choices people have at their local grocery store. It is easy to see how people can end up thinking: “If I buy the low-fat milk, I can have the hot dogs” (i.e. balancing conflicting goals). Furthermore, imagine all the unhealthy products that are presented as healthy under labels such as “natural”, “no added sugar” or “50% less fat”. These products prime people with conflicting goals automatically which provides people excuses for purchasing the salami with 30% fat content because it is promoted as having 50% less fat. Well, it may be healthier relative to salami with 60% fat, but it is still unhealthy.

Key reading(s):

Fishbach, A. & Zhang, Y. (2008). Together or apart: When goals and temptations complement versus compete. Journal of Personality and Social Psychology, 94, 547-559.

References:

Fishbach, A. & Dhar, R. (2005). Goals as excuses or guides: The liberating effect of perceived goal progress on choice. Journal of Consumer Research, 32, 370-377.

Fishbach, A., Friedman, R. S. & Kruglanski, A. W. (2003). Leading us not unto temptation: Momentary allurements elicit overriding goal activation. Journal of Personality and Social Psychology, 84, 296-309.

Stroebe, W., Mensink, W., Aarts, H., Schut, H. & Kruglanski, A. W. (2008). Why dieters fail: Testing the goal conflict model of eating. Journal of Experimental Social Psychology, 44, 26-36.

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Behaviour Change and Technology (BCT) is a blog written by 17 European designers and researchers.  BCT presents scientific news, reviews, and perspectives on how people shape technology and how technology shapes people in return. You can read more about us here.

We kick-off with a serial of three articles on goal adherence. Often, people find themselves surrounded in environments that can potentially compromise their personal goals. The question is how we can help people navigate in these environment that are not at odds with their personal long-term goals? As you will see, this has consequences for how we should present people with choices and how we represent goals.

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Happy readings and happy blogging!

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